{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from os import environ\n",
    "\n",
    "environ['optimizer'] = 'Adam'\n",
    "environ['num_workers']= '2'\n",
    "environ['batch_size']= str(2048)\n",
    "environ['n_epochs']= '1000'\n",
    "environ['batch_norm']= 'True'\n",
    "environ['loss_func']='MSE'\n",
    "environ['layers'] = '700 600 350 200 180'\n",
    "environ['dropouts'] = '0.5 '*5\n",
    "environ['log'] = 'False'\n",
    "environ['weight_decay'] = '0.01'\n",
    "environ['cuda_device'] ='cuda:2'\n",
    "environ['dataset'] = 'data/speedup_dataset2.pkl'\n",
    "\n",
    "%run utils.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "train_dl, val_dl, test_dl = train_dev_split(dataset, batch_size, num_workers, log=log)\n",
    "\n",
    "db = fai.basic_data.DataBunch(train_dl, val_dl, test_dl, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = train_dl.dataset.X.shape[1]\n",
    "output_size = train_dl.dataset.Y.shape[1]\n",
    "\n",
    "\n",
    "model = None \n",
    "\n",
    "if batch_norm:\n",
    "    model = Model_BN(input_size, output_size, hidden_sizes=layers_sizes, drops=drops)\n",
    "else:\n",
    "    model = Model(input_size, output_size)\n",
    "    \n",
    "if loss_func == 'MSE':\n",
    "    criterion = nn.MSELoss()\n",
    "else:\n",
    "    criterion = mape_criterion\n",
    "\n",
    "l = fai.Learner(db, model, loss_func=criterion, \n",
    "                metrics=[mape_criterion, rmse_criterion],\n",
    "                callback_fns=[partial(EarlyStoppingCallback, mode='min', monitor='rmse_criterion', min_delta=0, patience=10)])\n",
    "\n",
    "if optimizer == 'SGD':\n",
    "    l.opt_func = optim.SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = l.load(f\"speedup_{optimizer}_batch_norm_{batch_norm}_{loss_func}_nlayers_{len(layers_sizes)}_log_{log}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "l.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 1e-4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='194' class='' max='300', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      64.67% [194/300 06:00<03:16]\n",
       "    </div>\n",
       "    \n",
       "<table style='width:375px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>mape_criterion</th>\n",
       "    <th>rmse_criterion</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>4.849150</th>\n",
       "    <th>5.495269</th>\n",
       "    <th>122.653526</th>\n",
       "    <th>2.343617</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>4.835202</th>\n",
       "    <th>5.502480</th>\n",
       "    <th>123.398476</th>\n",
       "    <th>2.345316</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>4.811011</th>\n",
       "    <th>5.492928</th>\n",
       "    <th>122.334023</th>\n",
       "    <th>2.342560</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>4.808527</th>\n",
       "    <th>5.485877</th>\n",
       "    <th>121.958702</th>\n",
       "    <th>2.340786</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>4.738709</th>\n",
       "    <th>5.477128</th>\n",
       "    <th>120.836021</th>\n",
       "    <th>2.339912</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>4.704393</th>\n",
       "    <th>5.466801</th>\n",
       "    <th>119.736588</th>\n",
       "    <th>2.337562</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>4.678386</th>\n",
       "    <th>5.459597</th>\n",
       "    <th>118.855034</th>\n",
       "    <th>2.336057</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>4.670320</th>\n",
       "    <th>5.436223</th>\n",
       "    <th>116.281425</th>\n",
       "    <th>2.331513</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>4.629016</th>\n",
       "    <th>5.420029</th>\n",
       "    <th>114.505165</th>\n",
       "    <th>2.327652</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>4.581708</th>\n",
       "    <th>5.386214</th>\n",
       "    <th>110.691460</th>\n",
       "    <th>2.320695</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>4.570130</th>\n",
       "    <th>5.338640</th>\n",
       "    <th>105.269089</th>\n",
       "    <th>2.310129</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>4.484726</th>\n",
       "    <th>5.305558</th>\n",
       "    <th>101.504578</th>\n",
       "    <th>2.302952</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>4.450892</th>\n",
       "    <th>5.286104</th>\n",
       "    <th>99.310753</th>\n",
       "    <th>2.298952</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>4.464132</th>\n",
       "    <th>5.223441</th>\n",
       "    <th>93.360916</th>\n",
       "    <th>2.285407</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>4.353714</th>\n",
       "    <th>5.156177</th>\n",
       "    <th>90.604553</th>\n",
       "    <th>2.270307</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>4.324865</th>\n",
       "    <th>5.105012</th>\n",
       "    <th>89.490334</th>\n",
       "    <th>2.259230</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>4.292446</th>\n",
       "    <th>5.046215</th>\n",
       "    <th>89.039986</th>\n",
       "    <th>2.246210</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>4.191328</th>\n",
       "    <th>4.960802</th>\n",
       "    <th>89.588310</th>\n",
       "    <th>2.226851</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>4.149453</th>\n",
       "    <th>4.891520</th>\n",
       "    <th>91.295097</th>\n",
       "    <th>2.210925</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>4.093950</th>\n",
       "    <th>4.807950</th>\n",
       "    <th>94.590874</th>\n",
       "    <th>2.192035</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>21</th>\n",
       "    <th>4.022767</th>\n",
       "    <th>4.683848</th>\n",
       "    <th>100.618469</th>\n",
       "    <th>2.163907</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>22</th>\n",
       "    <th>3.946840</th>\n",
       "    <th>4.582268</th>\n",
       "    <th>106.995949</th>\n",
       "    <th>2.140493</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>23</th>\n",
       "    <th>3.876082</th>\n",
       "    <th>4.476713</th>\n",
       "    <th>114.908722</th>\n",
       "    <th>2.115050</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>24</th>\n",
       "    <th>3.775394</th>\n",
       "    <th>4.383356</th>\n",
       "    <th>122.564560</th>\n",
       "    <th>2.093547</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>25</th>\n",
       "    <th>3.706821</th>\n",
       "    <th>4.252637</th>\n",
       "    <th>134.998840</th>\n",
       "    <th>2.061933</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>26</th>\n",
       "    <th>3.605885</th>\n",
       "    <th>4.112627</th>\n",
       "    <th>148.940643</th>\n",
       "    <th>2.027636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>27</th>\n",
       "    <th>3.475561</th>\n",
       "    <th>3.974270</th>\n",
       "    <th>162.859451</th>\n",
       "    <th>1.993212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>28</th>\n",
       "    <th>3.409333</th>\n",
       "    <th>3.822030</th>\n",
       "    <th>179.371628</th>\n",
       "    <th>1.954649</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>29</th>\n",
       "    <th>3.274102</th>\n",
       "    <th>3.655406</th>\n",
       "    <th>195.926605</th>\n",
       "    <th>1.911699</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>30</th>\n",
       "    <th>3.164669</th>\n",
       "    <th>3.479872</th>\n",
       "    <th>217.408295</th>\n",
       "    <th>1.865000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>31</th>\n",
       "    <th>3.072425</th>\n",
       "    <th>3.311112</th>\n",
       "    <th>240.732819</th>\n",
       "    <th>1.819341</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>32</th>\n",
       "    <th>2.980420</th>\n",
       "    <th>3.165999</th>\n",
       "    <th>264.251038</th>\n",
       "    <th>1.778760</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>33</th>\n",
       "    <th>2.890625</th>\n",
       "    <th>3.032445</th>\n",
       "    <th>288.556030</th>\n",
       "    <th>1.740352</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>34</th>\n",
       "    <th>2.817683</th>\n",
       "    <th>2.870543</th>\n",
       "    <th>320.944946</th>\n",
       "    <th>1.694095</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>35</th>\n",
       "    <th>2.747082</th>\n",
       "    <th>2.715401</th>\n",
       "    <th>348.382965</th>\n",
       "    <th>1.647598</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>36</th>\n",
       "    <th>2.659433</th>\n",
       "    <th>2.564281</th>\n",
       "    <th>361.526794</th>\n",
       "    <th>1.600908</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>37</th>\n",
       "    <th>2.548133</th>\n",
       "    <th>2.349204</th>\n",
       "    <th>342.205719</th>\n",
       "    <th>1.532543</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>38</th>\n",
       "    <th>2.404953</th>\n",
       "    <th>2.106928</th>\n",
       "    <th>321.782715</th>\n",
       "    <th>1.451472</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>39</th>\n",
       "    <th>2.276161</th>\n",
       "    <th>1.995101</th>\n",
       "    <th>289.976990</th>\n",
       "    <th>1.412116</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>40</th>\n",
       "    <th>2.206393</th>\n",
       "    <th>1.962252</th>\n",
       "    <th>273.622986</th>\n",
       "    <th>1.400239</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>41</th>\n",
       "    <th>2.144551</th>\n",
       "    <th>1.968244</th>\n",
       "    <th>271.636566</th>\n",
       "    <th>1.402766</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>42</th>\n",
       "    <th>2.100734</th>\n",
       "    <th>1.957363</th>\n",
       "    <th>264.250916</th>\n",
       "    <th>1.398917</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>43</th>\n",
       "    <th>2.059998</th>\n",
       "    <th>1.955876</th>\n",
       "    <th>259.927246</th>\n",
       "    <th>1.398427</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>44</th>\n",
       "    <th>2.030568</th>\n",
       "    <th>1.950716</th>\n",
       "    <th>261.738983</th>\n",
       "    <th>1.396503</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>45</th>\n",
       "    <th>2.020180</th>\n",
       "    <th>1.953952</th>\n",
       "    <th>260.928741</th>\n",
       "    <th>1.397706</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>46</th>\n",
       "    <th>1.975701</th>\n",
       "    <th>1.976394</th>\n",
       "    <th>253.448395</th>\n",
       "    <th>1.405623</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>47</th>\n",
       "    <th>1.964468</th>\n",
       "    <th>1.945733</th>\n",
       "    <th>250.288956</th>\n",
       "    <th>1.394769</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>48</th>\n",
       "    <th>1.941859</th>\n",
       "    <th>1.925319</th>\n",
       "    <th>248.902832</th>\n",
       "    <th>1.387483</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>49</th>\n",
       "    <th>1.926990</th>\n",
       "    <th>1.924974</th>\n",
       "    <th>243.951248</th>\n",
       "    <th>1.387280</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>50</th>\n",
       "    <th>1.903308</th>\n",
       "    <th>1.920174</th>\n",
       "    <th>245.001450</th>\n",
       "    <th>1.385580</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>51</th>\n",
       "    <th>1.884465</th>\n",
       "    <th>1.905274</th>\n",
       "    <th>238.014297</th>\n",
       "    <th>1.380147</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>52</th>\n",
       "    <th>1.853729</th>\n",
       "    <th>1.899159</th>\n",
       "    <th>247.829742</th>\n",
       "    <th>1.377968</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>53</th>\n",
       "    <th>1.853921</th>\n",
       "    <th>1.909565</th>\n",
       "    <th>235.616806</th>\n",
       "    <th>1.381726</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>54</th>\n",
       "    <th>1.843833</th>\n",
       "    <th>1.898922</th>\n",
       "    <th>247.474045</th>\n",
       "    <th>1.377733</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>55</th>\n",
       "    <th>1.824959</th>\n",
       "    <th>1.900346</th>\n",
       "    <th>232.069275</th>\n",
       "    <th>1.378023</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>56</th>\n",
       "    <th>1.803379</th>\n",
       "    <th>1.887519</th>\n",
       "    <th>235.254272</th>\n",
       "    <th>1.373831</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>57</th>\n",
       "    <th>1.787041</th>\n",
       "    <th>1.877533</th>\n",
       "    <th>229.303497</th>\n",
       "    <th>1.370131</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>58</th>\n",
       "    <th>1.776168</th>\n",
       "    <th>1.888107</th>\n",
       "    <th>231.208572</th>\n",
       "    <th>1.373967</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>59</th>\n",
       "    <th>1.755344</th>\n",
       "    <th>1.890155</th>\n",
       "    <th>225.814117</th>\n",
       "    <th>1.374619</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>60</th>\n",
       "    <th>1.734962</th>\n",
       "    <th>1.872675</th>\n",
       "    <th>213.324478</th>\n",
       "    <th>1.368324</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>61</th>\n",
       "    <th>1.716865</th>\n",
       "    <th>1.879308</th>\n",
       "    <th>218.568771</th>\n",
       "    <th>1.370668</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>62</th>\n",
       "    <th>1.692745</th>\n",
       "    <th>1.856025</th>\n",
       "    <th>212.232269</th>\n",
       "    <th>1.362264</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>63</th>\n",
       "    <th>1.661845</th>\n",
       "    <th>1.860628</th>\n",
       "    <th>213.189880</th>\n",
       "    <th>1.363899</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>64</th>\n",
       "    <th>1.650244</th>\n",
       "    <th>1.826481</th>\n",
       "    <th>211.286407</th>\n",
       "    <th>1.351290</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>65</th>\n",
       "    <th>1.650749</th>\n",
       "    <th>1.816093</th>\n",
       "    <th>211.005203</th>\n",
       "    <th>1.347478</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>66</th>\n",
       "    <th>1.643249</th>\n",
       "    <th>1.795490</th>\n",
       "    <th>199.714020</th>\n",
       "    <th>1.339439</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>67</th>\n",
       "    <th>1.623431</th>\n",
       "    <th>1.765862</th>\n",
       "    <th>194.722031</th>\n",
       "    <th>1.328611</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>68</th>\n",
       "    <th>1.596583</th>\n",
       "    <th>1.766235</th>\n",
       "    <th>183.707275</th>\n",
       "    <th>1.328913</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>69</th>\n",
       "    <th>1.587757</th>\n",
       "    <th>1.797277</th>\n",
       "    <th>188.454620</th>\n",
       "    <th>1.340587</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>70</th>\n",
       "    <th>1.578291</th>\n",
       "    <th>1.739746</th>\n",
       "    <th>180.530975</th>\n",
       "    <th>1.318838</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>71</th>\n",
       "    <th>1.572501</th>\n",
       "    <th>1.713394</th>\n",
       "    <th>180.188507</th>\n",
       "    <th>1.308670</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>72</th>\n",
       "    <th>1.556570</th>\n",
       "    <th>1.689584</th>\n",
       "    <th>180.716721</th>\n",
       "    <th>1.299756</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>73</th>\n",
       "    <th>1.553234</th>\n",
       "    <th>1.793108</th>\n",
       "    <th>185.502213</th>\n",
       "    <th>1.338699</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>74</th>\n",
       "    <th>1.540929</th>\n",
       "    <th>1.721485</th>\n",
       "    <th>168.389755</th>\n",
       "    <th>1.311993</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>75</th>\n",
       "    <th>1.506934</th>\n",
       "    <th>1.677317</th>\n",
       "    <th>168.377243</th>\n",
       "    <th>1.294631</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>76</th>\n",
       "    <th>1.532782</th>\n",
       "    <th>1.765880</th>\n",
       "    <th>174.259796</th>\n",
       "    <th>1.328609</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>77</th>\n",
       "    <th>1.513720</th>\n",
       "    <th>1.728811</th>\n",
       "    <th>175.980270</th>\n",
       "    <th>1.314654</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>78</th>\n",
       "    <th>1.507654</th>\n",
       "    <th>1.694221</th>\n",
       "    <th>167.203278</th>\n",
       "    <th>1.301557</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>79</th>\n",
       "    <th>1.468045</th>\n",
       "    <th>1.594036</th>\n",
       "    <th>159.539963</th>\n",
       "    <th>1.262405</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>80</th>\n",
       "    <th>1.464609</th>\n",
       "    <th>1.694077</th>\n",
       "    <th>163.478073</th>\n",
       "    <th>1.300956</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>81</th>\n",
       "    <th>1.461843</th>\n",
       "    <th>1.589854</th>\n",
       "    <th>157.573380</th>\n",
       "    <th>1.260742</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>82</th>\n",
       "    <th>1.490199</th>\n",
       "    <th>1.800926</th>\n",
       "    <th>188.363968</th>\n",
       "    <th>1.341673</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>83</th>\n",
       "    <th>1.471706</th>\n",
       "    <th>1.733765</th>\n",
       "    <th>169.245926</th>\n",
       "    <th>1.316595</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>84</th>\n",
       "    <th>1.436064</th>\n",
       "    <th>1.587387</th>\n",
       "    <th>167.696396</th>\n",
       "    <th>1.259318</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>85</th>\n",
       "    <th>1.461508</th>\n",
       "    <th>1.753463</th>\n",
       "    <th>171.704514</th>\n",
       "    <th>1.324102</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>86</th>\n",
       "    <th>1.434334</th>\n",
       "    <th>1.688914</th>\n",
       "    <th>166.651474</th>\n",
       "    <th>1.299220</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>87</th>\n",
       "    <th>1.441644</th>\n",
       "    <th>1.755550</th>\n",
       "    <th>161.038895</th>\n",
       "    <th>1.324819</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>88</th>\n",
       "    <th>1.413921</th>\n",
       "    <th>1.732752</th>\n",
       "    <th>167.273132</th>\n",
       "    <th>1.316303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>89</th>\n",
       "    <th>1.413189</th>\n",
       "    <th>1.626374</th>\n",
       "    <th>156.806534</th>\n",
       "    <th>1.275040</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>90</th>\n",
       "    <th>1.387386</th>\n",
       "    <th>1.659163</th>\n",
       "    <th>156.171417</th>\n",
       "    <th>1.287864</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>91</th>\n",
       "    <th>1.374458</th>\n",
       "    <th>1.643735</th>\n",
       "    <th>149.589798</th>\n",
       "    <th>1.281786</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>92</th>\n",
       "    <th>1.354880</th>\n",
       "    <th>1.635321</th>\n",
       "    <th>148.904114</th>\n",
       "    <th>1.278177</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>93</th>\n",
       "    <th>1.364436</th>\n",
       "    <th>1.611316</th>\n",
       "    <th>161.039093</th>\n",
       "    <th>1.269151</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>94</th>\n",
       "    <th>1.340946</th>\n",
       "    <th>1.524836</th>\n",
       "    <th>154.051056</th>\n",
       "    <th>1.234677</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>95</th>\n",
       "    <th>1.358683</th>\n",
       "    <th>1.539093</th>\n",
       "    <th>153.491852</th>\n",
       "    <th>1.240504</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>96</th>\n",
       "    <th>1.346753</th>\n",
       "    <th>1.637271</th>\n",
       "    <th>152.036362</th>\n",
       "    <th>1.279453</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>97</th>\n",
       "    <th>1.324179</th>\n",
       "    <th>1.583750</th>\n",
       "    <th>148.770081</th>\n",
       "    <th>1.258234</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>98</th>\n",
       "    <th>1.330462</th>\n",
       "    <th>1.649743</th>\n",
       "    <th>154.850006</th>\n",
       "    <th>1.284232</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>99</th>\n",
       "    <th>1.331736</th>\n",
       "    <th>1.572363</th>\n",
       "    <th>156.732605</th>\n",
       "    <th>1.253355</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>100</th>\n",
       "    <th>1.319969</th>\n",
       "    <th>1.567499</th>\n",
       "    <th>157.350693</th>\n",
       "    <th>1.251963</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>101</th>\n",
       "    <th>1.308355</th>\n",
       "    <th>1.573833</th>\n",
       "    <th>152.964767</th>\n",
       "    <th>1.254122</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>102</th>\n",
       "    <th>1.307493</th>\n",
       "    <th>1.547395</th>\n",
       "    <th>155.253220</th>\n",
       "    <th>1.243779</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>103</th>\n",
       "    <th>1.291309</th>\n",
       "    <th>1.482207</th>\n",
       "    <th>151.393356</th>\n",
       "    <th>1.217247</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>104</th>\n",
       "    <th>1.264149</th>\n",
       "    <th>1.567032</th>\n",
       "    <th>152.174286</th>\n",
       "    <th>1.251487</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>105</th>\n",
       "    <th>1.272845</th>\n",
       "    <th>1.553355</th>\n",
       "    <th>156.314545</th>\n",
       "    <th>1.245890</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>106</th>\n",
       "    <th>1.278922</th>\n",
       "    <th>1.445401</th>\n",
       "    <th>150.635193</th>\n",
       "    <th>1.202119</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>107</th>\n",
       "    <th>1.305043</th>\n",
       "    <th>1.581411</th>\n",
       "    <th>154.478119</th>\n",
       "    <th>1.257466</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>108</th>\n",
       "    <th>1.281360</th>\n",
       "    <th>1.529868</th>\n",
       "    <th>165.261368</th>\n",
       "    <th>1.236705</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>109</th>\n",
       "    <th>1.261361</th>\n",
       "    <th>1.533704</th>\n",
       "    <th>160.148331</th>\n",
       "    <th>1.238103</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>110</th>\n",
       "    <th>1.251795</th>\n",
       "    <th>1.426174</th>\n",
       "    <th>153.860382</th>\n",
       "    <th>1.193992</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>111</th>\n",
       "    <th>1.258839</th>\n",
       "    <th>1.480716</th>\n",
       "    <th>157.362122</th>\n",
       "    <th>1.215721</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>112</th>\n",
       "    <th>1.258314</th>\n",
       "    <th>1.476176</th>\n",
       "    <th>151.818497</th>\n",
       "    <th>1.214671</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>113</th>\n",
       "    <th>1.240596</th>\n",
       "    <th>1.463387</th>\n",
       "    <th>148.366714</th>\n",
       "    <th>1.209527</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>114</th>\n",
       "    <th>1.225966</th>\n",
       "    <th>1.358139</th>\n",
       "    <th>150.509445</th>\n",
       "    <th>1.165170</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>115</th>\n",
       "    <th>1.193431</th>\n",
       "    <th>1.364513</th>\n",
       "    <th>150.963654</th>\n",
       "    <th>1.168066</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>116</th>\n",
       "    <th>1.180216</th>\n",
       "    <th>1.417164</th>\n",
       "    <th>152.159134</th>\n",
       "    <th>1.190220</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>117</th>\n",
       "    <th>1.161525</th>\n",
       "    <th>1.450819</th>\n",
       "    <th>151.724472</th>\n",
       "    <th>1.204424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>118</th>\n",
       "    <th>1.200765</th>\n",
       "    <th>1.393205</th>\n",
       "    <th>162.919861</th>\n",
       "    <th>1.179979</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>119</th>\n",
       "    <th>1.173354</th>\n",
       "    <th>1.318479</th>\n",
       "    <th>148.326721</th>\n",
       "    <th>1.147588</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>120</th>\n",
       "    <th>1.165755</th>\n",
       "    <th>1.289878</th>\n",
       "    <th>147.428894</th>\n",
       "    <th>1.135205</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>121</th>\n",
       "    <th>1.155131</th>\n",
       "    <th>1.386863</th>\n",
       "    <th>149.395660</th>\n",
       "    <th>1.177400</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>122</th>\n",
       "    <th>1.120504</th>\n",
       "    <th>1.376528</th>\n",
       "    <th>156.220551</th>\n",
       "    <th>1.172815</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>123</th>\n",
       "    <th>1.122893</th>\n",
       "    <th>1.347786</th>\n",
       "    <th>148.680969</th>\n",
       "    <th>1.160884</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>124</th>\n",
       "    <th>1.115108</th>\n",
       "    <th>1.443702</th>\n",
       "    <th>155.199051</th>\n",
       "    <th>1.201132</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>125</th>\n",
       "    <th>1.093863</th>\n",
       "    <th>1.246205</th>\n",
       "    <th>146.293640</th>\n",
       "    <th>1.116242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>126</th>\n",
       "    <th>1.066035</th>\n",
       "    <th>1.252740</th>\n",
       "    <th>147.977280</th>\n",
       "    <th>1.118924</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>127</th>\n",
       "    <th>1.073064</th>\n",
       "    <th>1.470992</th>\n",
       "    <th>153.839294</th>\n",
       "    <th>1.212733</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>128</th>\n",
       "    <th>1.098229</th>\n",
       "    <th>1.312681</th>\n",
       "    <th>154.091980</th>\n",
       "    <th>1.145467</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>129</th>\n",
       "    <th>1.077394</th>\n",
       "    <th>1.289419</th>\n",
       "    <th>148.798569</th>\n",
       "    <th>1.134883</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>130</th>\n",
       "    <th>1.079073</th>\n",
       "    <th>1.241984</th>\n",
       "    <th>148.079453</th>\n",
       "    <th>1.114287</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>131</th>\n",
       "    <th>1.058264</th>\n",
       "    <th>1.146939</th>\n",
       "    <th>158.174347</th>\n",
       "    <th>1.070774</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>132</th>\n",
       "    <th>1.066597</th>\n",
       "    <th>1.252612</th>\n",
       "    <th>151.693298</th>\n",
       "    <th>1.118727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>133</th>\n",
       "    <th>1.019600</th>\n",
       "    <th>1.191914</th>\n",
       "    <th>143.633255</th>\n",
       "    <th>1.091605</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>134</th>\n",
       "    <th>1.023774</th>\n",
       "    <th>1.286976</th>\n",
       "    <th>163.240005</th>\n",
       "    <th>1.134366</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>135</th>\n",
       "    <th>1.014537</th>\n",
       "    <th>1.228061</th>\n",
       "    <th>148.869232</th>\n",
       "    <th>1.108022</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>136</th>\n",
       "    <th>1.001702</th>\n",
       "    <th>1.135396</th>\n",
       "    <th>150.545395</th>\n",
       "    <th>1.065335</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>137</th>\n",
       "    <th>1.027857</th>\n",
       "    <th>1.189847</th>\n",
       "    <th>152.709030</th>\n",
       "    <th>1.090556</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>138</th>\n",
       "    <th>1.010530</th>\n",
       "    <th>1.132792</th>\n",
       "    <th>153.395081</th>\n",
       "    <th>1.063888</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>139</th>\n",
       "    <th>1.017043</th>\n",
       "    <th>1.363383</th>\n",
       "    <th>156.227219</th>\n",
       "    <th>1.167466</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>140</th>\n",
       "    <th>1.001750</th>\n",
       "    <th>1.302347</th>\n",
       "    <th>153.364456</th>\n",
       "    <th>1.140959</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>141</th>\n",
       "    <th>0.995828</th>\n",
       "    <th>1.229704</th>\n",
       "    <th>155.983246</th>\n",
       "    <th>1.108878</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>142</th>\n",
       "    <th>1.000850</th>\n",
       "    <th>1.344175</th>\n",
       "    <th>153.236969</th>\n",
       "    <th>1.159198</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>143</th>\n",
       "    <th>0.976650</th>\n",
       "    <th>1.176537</th>\n",
       "    <th>146.832703</th>\n",
       "    <th>1.084599</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>144</th>\n",
       "    <th>0.978683</th>\n",
       "    <th>1.248474</th>\n",
       "    <th>152.249588</th>\n",
       "    <th>1.116964</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>145</th>\n",
       "    <th>0.970618</th>\n",
       "    <th>1.056395</th>\n",
       "    <th>142.997726</th>\n",
       "    <th>1.027387</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>146</th>\n",
       "    <th>0.955093</th>\n",
       "    <th>1.185348</th>\n",
       "    <th>146.524796</th>\n",
       "    <th>1.088602</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>147</th>\n",
       "    <th>0.948485</th>\n",
       "    <th>1.091244</th>\n",
       "    <th>147.221527</th>\n",
       "    <th>1.044544</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>148</th>\n",
       "    <th>0.930368</th>\n",
       "    <th>1.195628</th>\n",
       "    <th>143.940720</th>\n",
       "    <th>1.093185</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>149</th>\n",
       "    <th>0.984236</th>\n",
       "    <th>1.229801</th>\n",
       "    <th>151.945862</th>\n",
       "    <th>1.108416</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>150</th>\n",
       "    <th>0.961103</th>\n",
       "    <th>1.247140</th>\n",
       "    <th>156.124496</th>\n",
       "    <th>1.116283</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>151</th>\n",
       "    <th>0.960714</th>\n",
       "    <th>1.168821</th>\n",
       "    <th>155.347092</th>\n",
       "    <th>1.080349</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>152</th>\n",
       "    <th>0.914923</th>\n",
       "    <th>1.163165</th>\n",
       "    <th>146.709793</th>\n",
       "    <th>1.078451</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>153</th>\n",
       "    <th>0.931650</th>\n",
       "    <th>1.195052</th>\n",
       "    <th>158.341919</th>\n",
       "    <th>1.093003</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>154</th>\n",
       "    <th>0.939162</th>\n",
       "    <th>1.183432</th>\n",
       "    <th>147.939774</th>\n",
       "    <th>1.087772</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>155</th>\n",
       "    <th>0.920542</th>\n",
       "    <th>1.044940</th>\n",
       "    <th>145.865585</th>\n",
       "    <th>1.022119</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>156</th>\n",
       "    <th>0.919162</th>\n",
       "    <th>1.149070</th>\n",
       "    <th>149.557236</th>\n",
       "    <th>1.071461</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>157</th>\n",
       "    <th>0.924206</th>\n",
       "    <th>1.226079</th>\n",
       "    <th>155.312454</th>\n",
       "    <th>1.106747</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>158</th>\n",
       "    <th>0.920060</th>\n",
       "    <th>1.192297</th>\n",
       "    <th>153.560120</th>\n",
       "    <th>1.091753</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>159</th>\n",
       "    <th>0.907668</th>\n",
       "    <th>1.249425</th>\n",
       "    <th>149.896637</th>\n",
       "    <th>1.117730</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>160</th>\n",
       "    <th>0.906381</th>\n",
       "    <th>1.125536</th>\n",
       "    <th>152.456909</th>\n",
       "    <th>1.060456</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>161</th>\n",
       "    <th>0.880608</th>\n",
       "    <th>1.148645</th>\n",
       "    <th>148.244781</th>\n",
       "    <th>1.071203</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>162</th>\n",
       "    <th>0.909835</th>\n",
       "    <th>1.149775</th>\n",
       "    <th>149.942322</th>\n",
       "    <th>1.072064</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>163</th>\n",
       "    <th>0.894779</th>\n",
       "    <th>1.096567</th>\n",
       "    <th>144.858032</th>\n",
       "    <th>1.047050</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>164</th>\n",
       "    <th>0.878538</th>\n",
       "    <th>1.038732</th>\n",
       "    <th>145.123795</th>\n",
       "    <th>1.018953</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>165</th>\n",
       "    <th>0.878073</th>\n",
       "    <th>1.057499</th>\n",
       "    <th>147.936920</th>\n",
       "    <th>1.028091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>166</th>\n",
       "    <th>0.873151</th>\n",
       "    <th>1.071648</th>\n",
       "    <th>152.988144</th>\n",
       "    <th>1.035168</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>167</th>\n",
       "    <th>0.870126</th>\n",
       "    <th>1.037174</th>\n",
       "    <th>148.510223</th>\n",
       "    <th>1.018225</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>168</th>\n",
       "    <th>0.879445</th>\n",
       "    <th>1.139968</th>\n",
       "    <th>153.444336</th>\n",
       "    <th>1.067114</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>169</th>\n",
       "    <th>0.881370</th>\n",
       "    <th>1.110640</th>\n",
       "    <th>149.783630</th>\n",
       "    <th>1.053255</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>170</th>\n",
       "    <th>0.861872</th>\n",
       "    <th>1.040420</th>\n",
       "    <th>147.973145</th>\n",
       "    <th>1.019872</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>171</th>\n",
       "    <th>0.856398</th>\n",
       "    <th>1.071994</th>\n",
       "    <th>151.886230</th>\n",
       "    <th>1.034573</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>172</th>\n",
       "    <th>0.863407</th>\n",
       "    <th>1.098345</th>\n",
       "    <th>145.842499</th>\n",
       "    <th>1.047668</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>173</th>\n",
       "    <th>0.853542</th>\n",
       "    <th>1.077948</th>\n",
       "    <th>148.346329</th>\n",
       "    <th>1.037941</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>174</th>\n",
       "    <th>0.843895</th>\n",
       "    <th>1.154110</th>\n",
       "    <th>153.644333</th>\n",
       "    <th>1.074082</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>175</th>\n",
       "    <th>0.837962</th>\n",
       "    <th>1.036494</th>\n",
       "    <th>148.208801</th>\n",
       "    <th>1.017684</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>176</th>\n",
       "    <th>0.837933</th>\n",
       "    <th>1.053644</th>\n",
       "    <th>156.984268</th>\n",
       "    <th>1.026289</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>177</th>\n",
       "    <th>0.826775</th>\n",
       "    <th>1.030625</th>\n",
       "    <th>150.514404</th>\n",
       "    <th>1.015093</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>178</th>\n",
       "    <th>0.830882</th>\n",
       "    <th>1.059734</th>\n",
       "    <th>146.220627</th>\n",
       "    <th>1.029196</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>179</th>\n",
       "    <th>0.837433</th>\n",
       "    <th>1.045840</th>\n",
       "    <th>150.572433</th>\n",
       "    <th>1.022515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>180</th>\n",
       "    <th>0.829252</th>\n",
       "    <th>1.038632</th>\n",
       "    <th>145.111755</th>\n",
       "    <th>1.018597</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>181</th>\n",
       "    <th>0.817792</th>\n",
       "    <th>1.028767</th>\n",
       "    <th>145.245575</th>\n",
       "    <th>1.014142</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>182</th>\n",
       "    <th>0.816613</th>\n",
       "    <th>1.027989</th>\n",
       "    <th>144.830521</th>\n",
       "    <th>1.013338</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>183</th>\n",
       "    <th>0.820276</th>\n",
       "    <th>1.037704</th>\n",
       "    <th>147.558380</th>\n",
       "    <th>1.018488</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>184</th>\n",
       "    <th>0.818847</th>\n",
       "    <th>0.976179</th>\n",
       "    <th>144.103622</th>\n",
       "    <th>0.987715</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>185</th>\n",
       "    <th>0.796234</th>\n",
       "    <th>1.057159</th>\n",
       "    <th>152.554382</th>\n",
       "    <th>1.027798</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>186</th>\n",
       "    <th>0.801649</th>\n",
       "    <th>1.014317</th>\n",
       "    <th>146.032272</th>\n",
       "    <th>1.007053</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>187</th>\n",
       "    <th>0.799823</th>\n",
       "    <th>1.077922</th>\n",
       "    <th>149.483856</th>\n",
       "    <th>1.038057</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>188</th>\n",
       "    <th>0.796435</th>\n",
       "    <th>1.041627</th>\n",
       "    <th>149.107300</th>\n",
       "    <th>1.020494</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>189</th>\n",
       "    <th>0.798958</th>\n",
       "    <th>1.040844</th>\n",
       "    <th>143.827057</th>\n",
       "    <th>1.019830</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>190</th>\n",
       "    <th>0.796220</th>\n",
       "    <th>1.013875</th>\n",
       "    <th>146.648331</th>\n",
       "    <th>1.006734</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>191</th>\n",
       "    <th>0.799817</th>\n",
       "    <th>1.047516</th>\n",
       "    <th>145.455231</th>\n",
       "    <th>1.023327</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>192</th>\n",
       "    <th>0.799169</th>\n",
       "    <th>1.065177</th>\n",
       "    <th>148.976120</th>\n",
       "    <th>1.031541</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>193</th>\n",
       "    <th>0.783658</th>\n",
       "    <th>1.054202</th>\n",
       "    <th>149.080826</th>\n",
       "    <th>1.026648</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>194</th>\n",
       "    <th>0.788463</th>\n",
       "    <th>1.055189</th>\n",
       "    <th>147.024918</th>\n",
       "    <th>1.027031</th>\n",
       "  </tr>\n",
       "</table>\n",
       "\n",
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='5' class='' max='5', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [5/5 00:00<00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 195: early stopping\n"
     ]
    }
   ],
   "source": [
    "l.fit_one_cycle(300, lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACOUAAAaPCAYAAADctchVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xuc13WB7/H3b4bhIncQL4AJUiqCCMN4SUm8d9RSM7e0db20Ztm2tetWa7WnrLaznVN5azt1ajfLbpaalVaamXctHZBQUcMLFiKChKBc5/I7f/wUQdFB+P3mO5fn8/GYx8z8Lt/fe3jof6/H51sql8sBAAAAAAAAAACqp67oAQAAAAAAAAAA0NOIcgAAAAAAAAAAoMpEOQAAAAAAAAAAUGWiHAAAAAAAAAAAqDJRDgAAAAAAAAAAVJkoBwAAAAAAAAAAqkyUAwAAAAAAAAAAVSbKAQAAAAAAAACAKhPlAAAAAAAAAABAlfUpesDGtt9++/K4ceOKngEAAAAAAAAAAJs1a9asZ8rl8qiOXtelopxx48alubm56BkAAAAAAAAAALBZpVLpiS15ndtXAQAAAAAAAABAlYlyAAAAAAAAAACgykQ5AAAAAAAAAABQZX2KHgAAAAAAAAAAwNZraWnJwoULs3bt2qKn9Cj9+/fP2LFj09DQsFXvF+UAAAAAAAAAAHRjCxcuzODBgzNu3LiUSqWi5/QI5XI5y5Yty8KFCzN+/PituobbVwEAAAAAAAAAdGNr167NyJEjBTlVVCqVMnLkyG06fUiUAwAAAAAAAADQzQlyqm9b/01FOQAAAAAAAAAAUGWiHAAAAAAAAAAAttqyZcsyderUTJ06NTvttFPGjBmz4ff169dv0TXOPPPMPPzwwzVe2rn6FD0AAAAAAAAAAIDua+TIkZkzZ06S5Pzzz8+gQYPy0Y9+dJPXlMvllMvl1NVt/vyYSy+9tOY7O5uTcgAAAAAAAAAAqLpHHnkkkydPzgc+8IE0Njbmqaeeytlnn52mpqZMmjQpn/vc5za8dsaMGZkzZ05aW1szbNiwnHfeedlnn33y5je/OUuWLCnwr9h6TsoBAAAAAAAAAOghPnvNA5m3aGVVr7nX6CH5zNsnbdV7582bl0svvTTf+MY3kiRf/OIXM2LEiLS2tubQQw/NSSedlL322muT96xYsSIzZ87MF7/4xZx77rn59re/nfPOO2+b/47O5qQcAAAAAAAAAABqYsKECdl33303/P6jH/0ojY2NaWxszIMPPph58+a94j0DBgzI0UcfnSSZPn16FixY0Flzq8pJOQAAAAAAAAAAPcTWnmhTKwMHDtzw8/z583PxxRfn7rvvzrBhw3Lqqadm7dq1r3hP3759N/xcX1+f1tbWTtlabU7KAQAAAAAAAACg5lauXJnBgwdnyJAheeqpp3L99dcXPammnJQDAAAAAAAAAEDNNTY2Zq+99srkyZOz22675aCDDip6Uk2VyuVy0Rs2aGpqKjc3Nxc9AwAAAAAAAACg23jwwQczceLEomf0SJv7ty2VSrPK5XJTR+91+yoAAAAAAAAAAKgyUQ4AAAAAAAAAAFSZKAcAAAAAAAAAAKpMlAMAAAAAAAAAAFUmygEAAAAAAAAAgCoT5QAAAAAAAAAAQJWJcgAAAAAAAAAA2GqHHHJIrr/++k0eu+iii/LBD37wVd8zaNCgJMmiRYty0kknvep1m5ubX/OzL7rooqxevXrD78ccc0yeffbZLZ1eU6IcAAAAAAAAAAC22imnnJLLL798k8cuv/zynHLKKR2+d/To0bnyyiu3+rNfHuX86le/yrBhw7b6etUkygEAAAAAAAAAYKuddNJJufbaa7Nu3bokyYIFC7Jo0aJMnTo1hx9+eBobG7P33nvn5z//+Sveu2DBgkyePDlJsmbNmpx88smZMmVK3v3ud2fNmjUbXnfOOeekqakpkyZNymc+85kkySWXXJJFixbl0EMPzaGHHpokGTduXJ555pkkyQUXXJDJkydn8uTJueiiizZ83sSJE/O+970vkyZNylFHHbXJ51RTn5pcFQAAAAAAAACAzvfr85LF91X3mjvtnRz9xVd9euTIkdlvv/1y3XXX5fjjj8/ll1+ed7/73RkwYECuvvrqDBkyJM8880wOOOCAHHfccSmVSpu9zte//vVst912mTt3bubOnZvGxsYNz33hC1/IiBEj0tbWlsMPPzxz587Nhz/84VxwwQW56aabsv32229yrVmzZuXSSy/NH/7wh5TL5ey///6ZOXNmhg8fnvnz5+dHP/pRvvWtb+Vd73pXrrrqqpx66qnV+bfaiJNyAAAAAAAAAADYJhvfwurFW1eVy+V88pOfzJQpU3LEEUfkySefzNNPP/2q17j11ls3xDFTpkzJlClTNjz3k5/8JI2NjZk2bVoeeOCBzJs37zX33H777XnHO96RgQMHZtCgQTnxxBNz2223JUnGjx+fqVOnJkmmT5+eBQsWbMuf/qqclAMAAAAAAAAA0FO8xok2tXTCCSfk3HPPzezZs7NmzZo0NjbmO9/5TpYuXZpZs2aloaEh48aNy9q1a1/zOps7Refxxx/Pl7/85dxzzz0ZPnx4zjjjjA6vUy6XX/W5fv36bfi5vr6+ZrevclIOAAAAAAAAAADbZNCgQTnkkEPy3ve+N6ecckqSZMWKFdlhhx3S0NCQm266KU888cRrXuPggw/OD37wgyTJ/fffn7lz5yZJVq5cmYEDB2bo0KF5+umn8+tf/3rDewYPHpznnntus9f62c9+ltWrV2fVqlW5+uqr85a3vKVaf+4WcVIOAAAAAAAAAADb7JRTTsmJJ5644TZWf/u3f5u3v/3taWpqytSpU7Pnnnu+5vvPOeecnHnmmZkyZUqmTp2a/fbbL0myzz77ZNq0aZk0aVJ22223HHTQQRvec/bZZ+foo4/OzjvvnJtuumnD442NjTnjjDM2XOOss87KtGnTanarqs0pvdZxPZ2tqamp3NzcXPQMAAAAAAAAAIBu48EHH8zEiROLntEjbe7ftlQqzSqXy00dvdftqwAAAAAAAAAAoMpEOQAAAAAAAAAAUGWiHAAAAAAAAACAbq5cLhc9ocfZ1n9TUQ4AAAAAAAAAQDfWv3//LFu2TJhTReVyOcuWLUv//v23+hp9qrgHAAAAAAAAAIBONnbs2CxcuDBLly4tekqP0r9//4wdO3ar3y/KAQAAAAAAAADoxhoaGjJ+/PiiZ/Aybl8FAAAAAAAAAABVJsoBAAAAAAAAAIAqE+UAAAAAAAAAAECViXIAAAAAAAAAAKDKRDkAAAAAAAAAAFBlohwAAAAAAAAAAKgyUQ4AAAAAAAAAAFSZKAcAAAAAAAAAAKpMlAMAAAAAAAAAAFUmygEAAAAAAAAAgCoT5QAAAAAAAAAAQJWJcgAAAAAAAAAAoMpEOQAAAAAAAAAAUGWiHAAAAAAAAAAAqDJRDgAAAAAAAAAAVJkoBwAAAAAAAAAAqkyUAwAAAAAAAAAAVSbKAQAAAAAAAACAKhPlAAAAAAAAAABAlYlyAAAAAAAAAACgykQ5AAAAAAAAAABQZaIcAAAAAAAAAACoMlEOAAAAAAAAAABUmSgHAAAAAAAAAACqTJQDAAAAAAAAAABVJsoBAAAAAAAAAIAqE+UAAAAAAAAAAECViXIAAAAAAAAAAKDKRDkAAAAAAAAAAFBlohwAAAAAAAAAAKgyUQ4AAAAAAAAAAFSZKAcAAAAAAAAAAKpMlAMAAAAAAAAAAFXWp+gB9ALlcrJ8QbKwOVk0Oxm0Q/LGI5MdJyWlUtHrAAAAAAAAAACqTpRD9a1dkTw5O3myuRLiLLwnWb2s8lyf/knr2uS35yeDRydvOjJ501HJbjOTfoMLnQ0AAAAAAAAAUC2iHLbdykXJ/N9U4puFs5KlDyUpV57bfo9k96OTsdOTsfsmoyYmq5Ymj/y28p77f5rM/m5S15Ds+uZKoPPGI5NRezhFBwAAAAAAAADotkrlcrnoDRs0NTWVm5ubi57B6/XQL5PL35MMGJGMbarEN2ObktGNyYBhr/3etpbkL3+oBDrzb0iWzKs8PvQNyYRDk132S8ZMT7bfPamrr/3fAgAAAAAAAADwGkql0qxyudzU4etEOWyzdc8nzz+djNht20+3efYvL5yic0Oy4LZk3crK430HJ6OnVgKdsU2V70NGb/t2AAAAAAAAAIDXQZRD99fenix7JHmyOXlyVuVr8X1Je2vl+cGjkzGNlUhn1xnJ6GlJvTuyAQAAAAAAAAC1s6VRjoKBrquuLhm1e+Vr6nsqj7WsrYQ5L4Y6C5uTh66tPNdvSDJuRrLbIcn4mcmoPbb95B4AAAAAAAAAgK0gyqF7aeif7LJv5etFq55JHr81efyW5LGbk4d/VXl80E7JbjNfinSGjilgMAAAAAAAAADQG4ly6P4Gbp9MPrHylSTLFySP3VKJdB65MZn748rjI9+UTDgs2fPYZNcDk/qGwiYDAAAAAAAAAD1bqVwuF71hg6ampnJzc3PRM+hJ2tuTJfMqJ+g8dnOy4LakdW3Sf1iy+1srgc6Ew5N+g4peCgAAAAAAAAB0A6VSaVa5XG7q6HVOyqFnq6tLdppc+TrwQ8n6VcmjNyUP/TL5068rp+jU96vc4mrPY5M9jk4G7VD0agAAAAAAAACgmxPl0Lv0HZhMfFvlq601+cvvK4HOQ9cm869Priklu+xXiXPecGCy8z5JQ/+iVwMAAAAAAAAA3YzbV0GSlMvJ0/cnD/2qEugsnlt5vK4h2WnvZGxTMnbfyvfh45NSqdi9AAAAAAAAAEAhtvT2VaIc2JznFicLm5OF9yRPzkqenJ20rKo8t93IZEzTC6FOU+VEHafpAAAAAAAAAECvsKVRjttXweYM3uml21wllVtdLX2oEuksbE6ebK7c7ipJRkxI3vmtZMz04vYCAAAAAAAAAF2Kk3Jga61dkTx+a/Lr85LnFyeHnJfMODepqy96GQAAAAAAAABQI1t6Uk5dZ4yBHqn/0GTi25Nz7kj2OiH53b8nlx6TLF9Q9DIAAAAAAAAAoGCiHNhWA4YlJ/13cuJ/JUvmJV+fkcz5YdKFTqECAAAAAAAAADqXKAeqZcrfVE7N2XlK8rNzkivOSFb/tehVAAAAAAAAAEABRDlQTcPekJx+TXL4Z5KHrk2+flDy2M1FrwIAAAAAAAAAOpkoB6qtrj55y7nJWTcm/QYllx2fXP+ppGVt0csAAAAAAAAAgE4iyoFaGT01OfuWZN+zkrv+M/nmzOSBnyXt7UUvAwAAAAAAAABqTJQDtdR3u+TYryTvuSJpa0muOD35+oHJfVcm7W1FrwMAAAAAAAAAakSUA51h96OSD92TvPO/k5STq/4++dp+yZwfVmIdAAAAAAAAAKBHEeVAZ6mrT/Y+KTnnruRvvpv0GZD87Jzkq9OTWd9JWtcXvRAAAAAAAAAAqBJRDnS2urpk0gnJB25LTrk82W5kcs1HkkumJXd/K2lZW/RCAAAAAAAAAGAbiXKgKKVSssfRyft+l5x6VTJ0bPKrjyYX75M8+rui1wEAAAAAAAAA20CUA0UrlZI3HpG897rk9GuS7UYkPzoleezmopcBAAAAAAAAAFtJlANdRamUjD84Of3aZMSE5IcnJwtuL3oVAAAAAAAAALAVRDnQ1QwcmZz282T4rskP3pU8cVfRiwAAAAAAAACA10mUA13RoFHJab9IhoxOfnBS8pe7i14EAAAAAAAAALwOohzoqgbvmJx+TTJoh+T770wWzip6EQAAAAAAAACwhUQ50JUN2bkS5gwYnnz/HcmiOUUvAgAAAAAAAAC2gCgHurqhY5Mzrk36DU0uOz5ZfF/RiwAAAAAAAACADohyoDsY9obk9F8kfQdVwpyn5xW9CAAAAAAAAAB4DaIc6C5GjK+EOfV9k+++PVnyUNGLAAAAAAAAAIBXIcqB7mTkhOT0a5O6+kqY88z8ohcBAAAAAAAAAJshyoHuZvs3Jqdfk6ScfP/EZO3KohcBAAAAAAAAAC8jyoHuaNQeyck/TFYsTK47r+g1AAAAAAAAAMDLiHKgu9plv+QtH03m/CCZ94ui1wAAAAAAAAAAGxHlQHc28+PJzlOTaz6SPLe46DUAAAAAAAAAwAtEOdCd1TckJ34raVmT/PxDSblc9CIAAAAAAAAAIKIc6P5G7Z4c9fnkkRuS5v8ueg0AAAAAAAAAEFEO9Az7npVMODy5/t+SZx4peg0AAAAAAAAA9HqiHOgJSqXk+K8lDf2Tn74vaWspehEAAAAAAAAA9GqiHOgphuycvO2iZNHs5NYvF70GAAAAAAAAAHo1UQ70JJNOSPY5Jbn1S8nC5qLXAAAAAAAAAECvJcqBnubo/50MGV25jdX6VUWvAQAAAAAAAIBeSZQDPU3/ock7vpH89fHk+k8VvQYAAAAAAAAAeiVRDvRE42YkB/5jMuvS5E/XF70GAAAAAAAAAHodUQ70VIf9W7Lj5OTnH0pWPVP0GgAAAAAAAADoVUQ50FP16Zec+M1k7bPJNR9JyuWiFwEAAAAAAABAryHKgZ5sx0nJ4Z9OHro2ufFzwhwAAAAAAAAA6CR9ih4A1NgB/5AseyS5/YLK74d/OimVit0EAAAAAAAAAD2cKAd6urq65NgLk5ReCHPKyeGfEeYAAAAAAAAAQA2JcqA3qKtLjn3hpJzbL6x8F+YAAAAAAAAAQM2IcqC3eDHMKZUqYU65nBxxvjAHAAAAAAAAAGpAlAO9SV1dcsxXkpSSOy5KUk6O+KwwBwAAAAAAAACqTJQDvU1dXXLsVyo/33Fx5bswBwAAAAAAAACqSpQDvVGptGmYUy4nR35OmAMAAAAAAAAAVSLKgd7qxTCnVEruvCRJOTny88IcAAAAAAAAAKgCUQ70ZqVScsyXk5SSO79aeUyYAwAAAAAAAADbTJQDvV2plBzzpcrPd341eX5pctS/J4NGFbsLAAAAAAAAALqxuqIHAF3Ai2HOwR9P7r8y+er05PdfT9pail4GAAAAAAAAAN2SKAeoKJWSwz6VnHNnMnZ6ct15yTdmJI/dXPQyAAAAAAAAAOh2RDnApkbtkZz60+TkHyYta5LLjk9+fGqy/ImilwEAAAAAAABAtyHKAV6pVEr2PDb5h7uTw/4teeTG5Gv7JTf9R7J+ddHrAAAAAAAAAKDLE+UAr66hf3Lwx5IP3ZPscUxyyxcrcc4DP0vK5aLXAQAAAAAAAECXJcoBOjZ0bPI3lyZn/DLpNyS54vTk++9M2lqKXgYAAAAAAAAAXZIoB9hy42Yk7781OeKzyaM3JrMvK3oRAAAAAAAAAHRJohzg9anvkxz0kWSXA5Jb/k+yfnXRiwAAAAAAAACgyxHlAK9fqZQccX7y/OLk7v9X9BoAAAAAAAAA6HJEOcDW2fXNyZuOSm6/MFmzvOg1AAAAAAAAANCliHKArXf4p5O1K5I7Lil6CQAAAAAAAAB0KaIcYOvttHcy+aTk919Pnltc9BoAAAAAAAAA6DJEOcC2OfSTSXtLcuuXil4CAAAAAAAAAF2GKAfYNiMnJI2nJbO+k/z1saLXAAAAAAAAAECXIMoBtt3BH0/qGpKb/qPoJQAAAAAAAADQJYhygG03ZOdk//cn912RLL6/6DUAAAAAAAAAUDhRDlAdM/4p6T8k+d3ni14CAAAAAAAAAIUT5QDVMWB4ctBHkj9dl/z590WvAQAAAAAAAIBCiXKA6tn/A8mgHZPfnp+Uy0WvAQAAAAAAAIDCiHKA6uk7MDn4Y8mf70rm31D0GgAAAAAAAAAojCgHqK7G05Ph45IbP5e0txe9BgAAAAAAAAAKIcoBqqtP3+TQTyVP35c88NOi1wAAAAAAAABAIUQ5QPVNPinZcXLyu39P2lqKXgMAAAAAAAAAnU6UA1RfXV1y2P9Mlj+ezL6s6DUAAAAAAAAA0OlEOUBt7P7WZJcDklv+T7J+ddFrAAAAAAAAAKBTiXKA2iiVkiM+kzy/OLnnW0WvAQAAAAAAAIBOJcoBamfXA5MJhyV3XOK0HAAAAAAAAAB6FVEOUFsz/zVZ/Uwy69KilwAAAAAAAABApxHlALX1hgOS8Qcnd1yctKwpeg0AAAAAAAAAdApRDlB7M/81ef7pZNZ3i14CAAAAAAAAAJ1ClAPU3rgZya4zkjsuSlrWFr0GAAAAAAAAAGpOlAN0jpkfT557Krn3e0UvAQAAAAAAAICaE+UAnWP8wckuByS3X5i0rit6DQAAAAAAAADUlCgH6BylUuW0nJVPJnN+UPQaAAAAAAAAAKgpUQ7QeSYcloxpSm67IGldX/QaAAAAAAAAAKgZUQ7QeUql5JDzkhV/SeZeXvQaAAAAAAAAAKgZUQ7Qud54RDJ6WnLrl5O2lqLXAAAAAAAAAEBNiHKAzlUqJTP/NXn2ieS+K4peAwAAAAAAAAA1IcoBOt/u/yPZaUpy65eSttai1wAAAAAAAABA1YlygM734mk5f30suf+qotcAAAAAAAAAQNWJcoBi7HFMsuPkymk57W1FrwEAAAAAAACAqhLlAMWoq0sO/liybH7ywNVFrwEAAAAAAACAqhLlAMWZeFwyauILp+W0F70GAAAAAAAAAKpGlAMUp64umfmxZOlDyYM/L3oNAAAAAAAAAFSNKAco1l4nJNvvntzitBwAAAAAAAAAeg5RDlCsuvrk4I8lSx5IHv5l0WsAAAAAAAAAoCpEOUDxJp2YjJiQ3PS/kraWotcAAAAAAAAAwDYT5QDFq++THPm5ZMm85M5Lil4DAAAAAAAAANtMlAN0DRPflux1fHLz/06W/qnoNQAAAAAAAACwTUQ5QNdx9JeShgHJL/4xaW8veg0AAAAAAAAAbDVRDtB1DN4x+R//kfzl90nzfxe9BgAAAAAAAAC2migH6Fr2OSWZcFjy2/OTZ/9c9BoAAAAAAAAA2CqiHKBrKZWSt12UlMvJtf9c+Q4AAAAAAAAA3YwoB+h6hu+aHPGZ5JHfJnN/XPQaAAAAAAAAAHjdRDlA17TvWcku+yfXnZc8v6ToNQAAAAAAAADwuohygK6prj457qvJ+lXJrz9e9BoAAAAAAAAAeF1EOUDXNWqPZObHkweuTh68tug1AAAAAAAAALDFRDlA13bQPyU7Tk5++S/JmmeLXgMAAAAAAAAAW0SUA3Rt9Q3J8f+ZrFqS3PDpotcAAAAAAAAAwBYR5QBd3+hpyYH/mMz+bvLYLUWvAQAAAAAAAIAOiXKA7uGQTyQjdkuu+XCyfnXRawAAAAAAAADgNYlygO6hYUBy3FeT5QuSm75Q9BoAAAAAAAAAeE2iHKD7GDcjaXpv8vv/myy6t+g1AAAAAAAAAPCqRDlA93LE+cmAEcl1n0zK5aLXAAAAAAAAAMBmiXKA7qX/0OSwTyV/vjN58Jqi1wAAAAAAAADAZolygO5n2mnJDnslN3w6aV1X9BoAAAAAAAAAeAVRDtD91PdJjvr3ZPnjyd3fLHoNAAAAAAAAALyCKAfont54ePKmo5JbvpSsWlb0GgAAAAAAAADYhCgH6L6O/Hyy/vnk5v8oegkAAAAAAAAAbEKUA3RfO+yZNJ2ZNH87Wfpw0WsAAAAAAAAAYANRDtC9HfKJpO+g5Df/s+glAAAAAAAAALCBKAfo3gZunxz80WT+9cmjvyt6DQAAAAAAAAAkEeUAPcH+70+Gj0uu/1TS3lb0GgAAAAAAAAAQ5QA9QJ9+yZGfS5bMS2ZfVvQaAAAAAAAAABDlAD3ExOOSNxyY3PSFZO3KotcAAAAAAAAA0MuJcoCeoVRK3vqFZNXS5PYLi14DAAAAAAAAQC8nygF6jjGNyZSTk7u+lix/oug1AAAAAAAAAPRiohygZzn800mpLrnxs0UvAQAAAAAAAKAXE+UAPcvQMclBH07uvyr5y91FrwEAAAAAAACglxLlAD3PgR9OBu2UXPeJpFwueg0AAAAAAAAAvZAoB+h5+g2q3MbqyebKiTkAAAAAAAAA0MlEOUDPtM8pyQ6TktsvdFoOAAAAAAAAAJ1OlAP0THV1yb5/nzx9f7Lo3qLXAAAAAAAAANDLiHKAnmvvk5I+A5LZlxW9BAAAAAAAAIBeRpQD9Fz9hyaTTkjuvypZv7roNQAAAAAAAAD0IqIcoGeb9nfJupXJvJ8XvQQAAAAAAACAXkSUA/Rsux6YjJjgFlYAAAAAAAAAdCpRDtCzlUpJ498lf74zeeaRotcAAAAAAAAA0EuIcoCeb5/3JKX65F6n5QAAAAAAAADQOUQ5QM83eMdk97cmc36UtLUUvQYAAAAAAACAXkCUA/QOjaclq5Yk839T9BIAAAAAAAAAegFRDtA7vPHIZNBOyWy3sAIAAAAAAACg9moa5ZRKpQWlUum+Uqk0p1QqNdfyswBeU32fZOp7KiflrFxU9BoAAAAAAAAAerjOOCnn0HK5PLVcLjd1wmcBvLpppybl9mTOD4teAgAAAAAAAEAP5/ZVQO8xckKy64zk3u8n7e1FrwEAAAAAAACgB6t1lFNO8ptSqTSrVCqdXePPAuhY42nJ8seTJ+4oegkAAAAAAAAAPVito5yDyuVyY5Kjk/xDqVQ6+OUvKJVKZ5dKpeZSqdS8dOnSGs8Ber29jkv6DU1mX1b0EgAAAAAAAAB6sJpGOeVyedEL35ckuTrJfpt5zTfL5XJTuVxuGjVqVC3nACQNA5K9T0oe/EWyZnnRawAAAAAAAADooWoW5ZRKpYGlUmnwiz8nOSrJ/bX6PIAt1nha0ro2ue/KopcAAAAAAAAA0EPV8qScHZPcXiqV/pjk7iS/LJfL19Xw8wC2zOipyU57u4UVAAAAAAAAADXTp1YXLpfLjyXZp1bXB9gmjacnv/posmhOJdIBAAAAAAAAgCqq5Uk5AF3X3icl9f2Se79X9BIAAAAAAAAAeiBRDtA7DRie7HVcMveKpGVN0WsAAAAAAAAA6GFEOUDv1Xhasm5FMu8XRS8BAAAAAAAAoIcR5fCqLrzhT3n7V28vegbUzq4zkuHj3MIKAAAAAAAAgKoT5fCqLr5xfu57ckXRM6B26uqSaacmC25Llj1a9BoAAAAAAAAAehBRDtC7Tf3bpFSX3Pv9opcAAADJAmCVAAAgAElEQVQAAAAA0IOIcoDebcjo5I1HJnN+mLS1FL0GAAAAAAAAgB5ClAPQ9N7k+cXJ/VcVvQQAAAAAAACAHkKUA/Cmo5IdJiW3XZC0txe9BgAAAAAAAIAeQJQDUFeXvOXc5JmHk4euLXoNAAAAAAAAAD2AKAcgSSa9IxmxW3Lbl5Nyueg1AAAAAAAAAHRzohyAJKmrT2b8c/LUH5NHbyx6DQAAAAAAAADdnCgH4EVTTk6GjElu/UrRSwAAAAAAAADo5kQ5dGh9a3tO/uZduffPy4ueArXVp29y4IeTP9+ZPHFn0WsAAAAAAAAA6MZEOXRo/pLn8vvH/ppPXn1/0VOg9hpPS7bbPrnNaTkAAAAAAAAAbD1RDh0qlyvfS8XOgM7Rd7vkzR9MHvltsujeotcAAAAAAAAA0E2JcujQhijnhSpn1brWLF+1vrhBUGv7npX0G5rcdkHRSwAAAAAAAADopkQ5dKicSpXzwKKVSZLDvnJzpn3+hjyxbFWRs6B2+g9N9ntf8uA1ydKHi14DAAAAAAAAQDckyqFDL56U86KnV65Lksz80s2dPwY6ywEfTBoGJLdfWPQSAAAAAAAAALohUQ4dan95lQO9wcCRyfQzkrk/SZYvKHoNAAAAAAAAAN2MKIcOtWty6K0O/MekVJfccUnRSwAAAAAAAADoZkQ5bIGXqpy1LW2bPuMUHXqyIaOTqe9J7v1+8tziotcAAAAAAAAA0I2IcujQxiflLFy+ZpPn1rW2d/Ia6GQz/ilpb0nu+s+ilwAAAAAAAADQjYhy6FDbJvev2vRknJ/OfrJzx0BnG7FbMvmdyT3fTlb/teg1AAAAAAAAAHQTohw6tHGU8/KTcX46e2Fnz4HON+PcpGVV8of/V/QSAAAAAAAAALoJUQ4dat0oyln/QpTTt77yn07zE8s3ee337lqQcef9Ms+va+20fVBzO+6V7HFs8odvJOueK3oNAAAAAAAAAN2AKIcOtbW/dDrOUyvWJkl2GTFgw2PtG0U7/3X740mS2+c/s0XXXrG6Jbt94pd5eLHQgS7u4H9J1j6bNH+76CUAAAAAAAAAdAOiHDrU2vZSdLN6fVuS5OKTp730WEvbhp/3HjM0SbKu9aXHXsuFv/1T2svJRy6/txpToXbGTE92OzS58z+TljVFrwEAAAAAAACgixPl0KGNb1/V2lY5NWf4wL4bHlu10a2qBjTUJ0lWrt2y21e9YcR2SZJ9x43Y5p1Qc2/5l2TVkmTOD4teAgAAAAAAAEAXJ8qhQxtHOWteOBWnoa604bHnNgpwtutbiXIWLl+9Rdd+8TKl0mu/DrqEcTOSnfep3MKqXO749QAAAAAAAAD0WqIcOtTW3r7h589eMy9J0qf+pf90Nj4p57t3PZEkeXTJqg6ve8O8p3P+C9fTN9AtlEpJ4+nJ0/cnT84ueg0AAAAAAAAAXZgohw61tL2ymKnf6KSca/64KElS3qis+e2DT2fF6pbXvO77Lmuu0kLoRHv/TdKwXTL7O0UvAQAAAAAAAKALE+XQodbNRDl96kq58gNvTpL81+2PJ0lWr2/b5DXv/e49W/wZ3/v9E1nf2t7xC6Fo/Yckk09M7rsqWbuy6DUAAAAAAAAAdFGiHDrU2v7KWGZgvz7ZbdSgTR57fqPbWCXJrCeWv+o117W2veKxf7nij1u5EDrZ9DOTllXJ/VcWvQQAAAAAAACALkqUQ4defvuq/cePSJKMGNg340Zul7dN2Tmtbe15asXaJMmxU3ZOkowZNuBVr3njg0te8diLt8GCLm/M9GTHycms7xa9BAAAAAAAAIAuSpRDh1rbNj0pp6H+pf9s+jfUZ31rez56xR9zwtfuSJKcOG1MJu48JHvuNPhVr3nLw0s3/HzWjPFVXgw1VioljacnT81JFt1b9BoAAAAAAAAAuiBRDh1qbd/0pJyG+tKGn9vay/nNvKfzszkvnXKzw+D+2X5Q39z40JJc/8DizV7zx81/2fDz8IF9M2y7huy185AqL4camvKupE9/p+UAAAAAAAAAsFmiHDrU8rKTcvpsdFLO/CXPv+L1e48dmlGD+iVJ3v+9Wa94/pnn123ye3t7OQdN2D7rX/Y50KUNGJZMOjG578pk3Sv/PwAAAAAAAACgdxPl0KGXRzl96zv+z2b1+rZXPPbw4ufy5LNr8qfFz23yeHu5Euo8suT5V3wWdGnTT0/WP5c88NOilwAAAAAAAADQxYhy6FBr26a3ryqVXuWFG7luM7eteutFt+bvv3NP2sqbXm+fXYbmD4//NUkyd+GKrR8KnW2X/ZNRe7qFFQAAAAAAAACvIMqhQy1t5fSpK230+0un2ew/fsQmrx2//cAkySeO3nOz13po8XNZ88IpOkdM3CF3f+rwHLLHDtlzp8FJkuYFf63qdqipUilpPD15sjlZfF/RawAAAAAAAADoQkQ5dKi1vT196l+Kcta3vhTl/Pj9b97ktY8/sypJcuieO2x6jY1CnjUtlSjnE8dMzA6D+ydJvnPmfkmSe//8bBWXQyfY5+Skvp/TcgAAAAAAAADYhCiHDlVOyqnLxSdP3fB7R7brW7/J72s3CnlePClnQMNLr9lpaP8MaKjPG0ZuV43J0Hm2G5HsdXwy9yfJ+tVFrwEAAAAAAACgixDl0KHWtvbU15UysG+fJJuelPNyb5uyc5JkzLABqX/hlle/uu+prH3hdJwkaX5ieZJkYL8+m7x3yIA+WbG6ZcPvdz26LN+89dHq/BFQS9NPT9atSP4/e3caX+dZn4n/OtotS97txPuWxXZih5DVCQ5JWENCAmVpoRu0U4Yp7XTKFEr5tx2gU6AznTK00ylTOp20ZWmhhbIFCgFC9oWEbCTO5tiOkziOE++WZC3n/+Ikx1YkR7Jj6dGRvt83z/08z32OLudj602uz+++71+LTgIAAAAAAADAGKGUw5B6+sppqCtl0nPTbw709i/lrJw7pbr+07edliQplUr59QuXJ0l+/fN3VKfjJMk/374lSTKlpX8p56ndXfmnHz+Wcrkyiecdn705H79q/TH+08AIWHx+MvOE5PYri04CAAAAAAAAwBihlMOQup+blNPUMPhflyvffVaS5PcuWZGWQ46kuu+J3dX19r1d/T4zp705pVJp0O/b1dHd737b7s709Pbl1X/2o/zbT7ce1Z8BRlSplJzxruSxW5Jt9xedBgAAAAAAAIAxQCmHIfX0Vibl1B2mRHPclJas/6PX5z0XLOv3/KYNz1TX7/jszf3ebdvTv6RzqJd97HvpOWQaz4e/ek92d/bk4W1785+/dNfR/BFg5J32jqSuMbn974pOAgAAAAAAAMAYoJTDkG7b+Gye2NWZw3RykiQtjfUDJt+0H3I8VWd33ws/MsCfvGV1df3I0/uq66vv35aunsrxV3u7enKgZ+jvglE3eVay8o3JXV9MujuLTgMAAAAAAABAwZRyGNIz+w4kSToPVIoxLY3D+2vT1tww9KZDLJ/dVl3/7fWPVtez2prymj+7tnq/s6OSp7evnP/6zfvy5K6OI/o5MGLO+OWkc2dy/9eLTgIAAAAAAABAwZRyGLYTj2tPkvzaumVD7Kxob2k87Ls57c0Dnk1rbaqu/+nHj1XX2/ceyN6unur97o7K+scbn83fXP9ofufLB4+02rW/Ow8+tSe/9vc/ztfufHxYOeGYWXJBMn1pcvuVRScBAAAAAAAAoGBKOQzpuCnNOXfZjMxub87GT16aV608blife/mi6UmSlXOnJElef8rx+fJ71+a4Kc257ncvGrB/+ezJ+Y+vOrHfs8GOzHr/l+5Mknz8qvuTJDc8/Ex+uH5bkuStn7kxr/3UtfnefU/lt/7xzuH9AeFYqaurTMvZdEOy/aGi0wAAAAAAAABQIKUchtRQV5fWpiM7iipJPnTJinz5vWtz2Zq5SSp9hbOWzMgtH351mhvqB+wvlUp5/2tO6vesXB74vXdv2ZUkueu5a5K8+8rbkiQPbdv7gs8P8gUwkl7280ldg2k5AAAAAAAAABOcUg5D6iuXM8jAmiE1NdTlrCUzMqmxUsDp6xv+54ZSLpfT0jj0vheWdGDEtc1JTn5DcucXkp6uotMAAAAAAAAAUBClHIbUVy4PeozUcLU1V6bs9A5zas3LFkyrrtubD07o+fy/O6e67urpS98wvu4lxIajd8YvJx3PJuu/WXQSAAAAAAAAAAqilMOQevsqR0sdrfaWSrGmp3d4o3JWzZtSXR86Naeh7mCGru6+HOgZ+vtufvTZ4caEY2fZxcnURY6wAgAAAAAAAJjAlHIYUvkoj6963qSmyvFVPcMZbZPkg68/OUlywpy2LJzROuiezp7eYX3XH3/rvmHtg2Oqri5Z8/Zk4w1J566i0wAAAAAAAABQAKUchvRSj69qrK/8NesaxmSbJGltasjGT16aq9//ynz2l86sPt/ffbCIc8PD2wd8rrN7YFGns7svH/n6T480Mrx0yy9Kyr3JxuuLTgIAAAAAAABAAZRyGFJvXzmllzArZ/HMyrSbMxZPP+LPzm5vzh+96dTKuq25+vz9X7prwN5Hnt476HdceePGI/658JItODtpbE0e+WHRSQAAAAAAAAAogFIOQ+orV07jOVoLprfmRx+4MO9/zUlH9flfPHdxrvvgRTl1/tT8zSGTc57339+6JkmybXfXoJ8/e+mMo/q58JI0NCWLz082XFN0EgAAAAAAAAAKoJTDkPrKL21STpIsnjm5eozV0Vg4ozJtp7Wpvt/z//7WNTl+akuS5ENfubvfuz97+2mZP21SFk5vPeqfCy/J8ouSZx5Kdm0pOgkAAAAAAAAAo0wphyH19pXzEjs5x8yMtqbqet7UlrztzIVpb2lMkjz1gkk5W3d3ZnJzffYf6BnVjFC17MLK1bQcAAAAAAAAgAlHKYchlctJXWlstHJWHD+lur50zdwkSXtLQ5Jk/rRJ/faWy0lrU0P2dinlUJA5q5LJc5RyAAAAAAAAACYgpRyGVDm+auw5fmqlhDPluUk5XT29/d7/+wuWpbO7N9c9tH3Us0GSpFSqTMvZcE3S11dwGAAAAAAAAABGk1IOQ+otlzNGBuUkSdYsmJokOX5KS5KDk3K27z1QXSdJQ31d1m/dkyT5zI8eGeWU8JxlFyb7nk623Vd0EgAAAAAAAABGkVIOQxpLx1clyd1bdiVJpk6qTMhpaaxPU0Plr/Kezv5HVZ1/wswkySe/vX4UE8Ihll1YuW74YZEpAAAAAAAAABhlSjkMy9ip5CSf+tnTkhws3CTJ7Lbm6vrKd5+Vj11xSpLk7WcuHN1w8EJT5yezTqocYQUAAAAAAADAhKGUw/CMoVbOm09fkI2fvDSlQ6b3PL6zI0nyznMW5cKT5+SX1i5Jklx+2rwkycsXTRv1nFC17KJk041JT1fRSQAAAAAAAAAYJUo5DMtDT+0tOsKw9PaW+92XSqW88qTZecFjGF3LLky69yeP3Vp0EgAAAAAAAABGiVIOw3LP47uKjvCi3vjcRJyG+oEjfZoa6tLV3Zuv3fl4Tv/Yd9Pd2zfa8ZjolpyflOodYQUAAAAAAAAwgSjlMC6cuXh6kqS+bmApp7u3L+u37slHv3Ffduzvzo79B0Y7HhNdy9RkwZnJhh8WnQQAAAAAAACAUaKUw7gwZVJDkmTapMYB73bs706S1JUqhZ2tuzpHLxg8b9mFyRM/STp2FJ0EAAAAAAAAgFGglMO48PpT5uYDrzs5771w+YB3l62emyTZvrcrSfL+L901qtkgSbLsoqTcl2y8vugkAAAAAAAAAIwCpRzGhUlN9XnfRSektalhwLvW5vp+9xue3jtaseCgBWcmTW3JI46wAgAAAAAAAJgIlHIY9ya/oKjTVy4oCBNbfWOy+PxkwzVFJwEAAAAAAABgFCjlMCx/cNmqoiMctcnN/Us57zh7YUFJmPCWX5Q8+0iyc3PRSQAAAAAAAAAYYUo5DMvJx7UXHeGotbf0L+WUTcqhKMsurFxNywEAAAAAAAAY95RyGJb6ulLREY7atNbGfvd7unoKSsKEN3tF0na8Ug4AAAAAAADABKCUw7A01NduKae1sf+knL2dSjkUpFSqTMvZcE3S11dwGAAAAAAAAABGklIOw1LLk3LmTmuprlub6rPPpByKtOzCZP8zyVP3Fp0EAAAAAAAAgBGklMNhrZw7pbquL9VuKaexvi5ffu/a/OyZC3P+CbOys6M7V93zZMrlcr99O/YdyANb9yRJ9nR2Z9f+7iLiMt4tu7By3fDDIlMAAAAAAAAAMMKUcjis97/mpOq6liflJMlZS2bkT966Ju0tDXl42978+ufvyOdv2Zwv3rq5Ws5595W35XX/89rs6ezO6o98N6d97LsFp2ZcmjI3mb2icoQVAAAAAAAAAONWQ9EBGLue3NVRXTfU13Yp53ltzQf/yv/+v1aOD5o3bVJeedLs3PnYziTJ6o8o4zDCll2U3H5l0t2ZNLYMuR0AAAAAAACA2mNSDod13xO7q+uGGp+U87ypkxoHPPvlv701yeB/xs7u3hHPxAS07MKkpyN57JaikwAAAAAAAAAwQpRyGJaGuvHxV2XG5KbDvmttqh/w7KndnSMZh4lqyflJXYMjrAAAAAAAAADGsfHRtGBElMsH15MGKazUoj2dPYM+7+nty56uge92dwy+H16S5vZkwVnJhh8WnQQAAAAAAACAEaKUw2GVc7CV09IwPko5LY2D/5X/g6/d26+E9Lzf/OId2dXRPcKpmJCWXZg8cWey/9mikwAAAAAAAAAwApRyOKxDSypNDePjr8pFJ88Z9PkXb32sup7VdvCIq43P7M9pH/1uunp6RzwbE8yyi5KUk43XFZ0EAAAAAAAAgBEwPpoWjIhDB8fU15UKy3EsnXhcezZ8/A3V+587a2G/923NDXnz6fMHfO7iP/3REf+sX/7bW3PlDY8eeUgmhvkvT5rak0ccYQUAAAAAAAAwHinlcFiHTsoZL6WcJKk75M9yoKev37sVx7fnQ5eszNfed36/54/v7Djin/OjB5/OR75x39GFZPyrb0yWvCLZcE3RSQAAAAAAAAAYAUo5HFb5kFk546iTU7V6/tT84RtX9Xu2dvnM1NeVcur8qf2en7tsxhF99wvLPjCo5RclOx5NdmwsOgkAAAAAAAAAx5hSDoe1au6U6rpUGl+tnOs+eFH+8T3nZlprU/XZO85elP/06pOSVCYDfeYXXp7v/vYFSZKbNzx7RN+/q6P72IVl/Fp2YeVqWg4AAAAAAADAuKOUw2G98bR5RUcYMQtntGZyc0O/Z5/4mdX9jul6/alzc9Jx7Uf1/Xs6lXIYhlknJe3zkkd+WHQSAAAAAAAAAI4xpRwOa5wNxzlqbzxtXpbMbD2iz3R0945QGsaVUqkyLefRHyV9jjwDAAAAAAAAGE+UcjisUiZGK+ef37s2n/65lx32/ZSWhuzu7BnwvKe3L5+9dkM6ByngdBw4+OyMP/resQnK+LT8oqRjR7L1rqKTAAAAAAAAAHAMNQy9hYmqbmJ0cnLmkhk580Xet7c0Zk9nd8rlckqHjA86/WPfy56unuw/0JvfevWJ/T5z6KScZ/YdSF9fOXUT5T8oR2b5xUmpLll/VTLv9KLTAAAAAAAAAHCMmJTDYZWcX5UkaW9pSHdvOV09/Y8X2tNVmZ7zqasf7Pd8+96u/Ocv9Z968o+3PTayIaldk2cli89P7vta0UkAAAAAAAAAOIaUcjgslZyKKZMakyS7O7oHfT9jclO/+/959YPZtqer37MPf/WerP3E90cmILVv1RXJ9geSbeuLTgIAAAAAAADAMaKUw2HVmZSTJJneWinl7NjfnU9978F8596t/d6/fNH07Np/sLBz6H+3WW0HCztP7uoc4aTUrBWXJSkl93+96CQAAAAAAAAAHCNKORyeTk6SpL2lUsr5v9dvyKe//1De+7nb88MHtqWlsfLP5+r7n8ppH/tu7ntid5Kksf7gP6sffeCi0Q9M7ZkyN1l4TnKfUg4AAAAAAADAeKGUw2EZlFPRcaA3SfKlH2+pPnv3/7stnd19/fY9sbMjF/73H+b/Xv9o9VlrU/3ohKT2rboieeqe5JlHik4CAAAAAAAAwDGglMNh6eRU9PaVh7Wvq6cvG5/Z3+9ZqVTK2mUzB91/+6Znc+/ju15yPsaJlW+sXO/7WrE5AAAAAAAAADgmlHI4rGF2Uca9E+a0DWvf1t2dgz6/8OTZ1fX6rZUjrm58ZHve8lc35bK/uP6w37eroztLPvStfOp7Dx5BWmrWtIXJ/DOS+x1hBQAAAAAAADAeKOVwWOWyVk6SnHx8ez7wupOH3Ld1V8egz3/1FUur69/957vT11fOf/rHO4f8vke370uSfPr7Dw0zKTVv5eXJEz9JdmwqOgkAAAAAAAAAL5FSDofV3tJYdIQxY+msyUPueXJX/0k5l66ZmyRpqK/Lbf/fq5Mkd23ZlR8+sC0r5k4Z8vs6u3uPIik1bdXllev93yg2BwAAAAAAAAAvmVIOh1VfVyo6wpgxddLQBaVv3v1kdf0fX3Vi/vKdL6/ez25vrq5/9e9+nPnTWqr3h5tIdOPD26vrL9yy+YjyUqNmLEuOX5Pc97WikwAAAAAAAADwEinlwDC0NTcM+vwLv3ZO7vovr+3/7N+dk//0qhMH7D11/sHpOF+89bHqendnz6Df/ec/eLi6/vBX7zmivNSwVZcnW25Ndj1edBIAAAAAAAAAXgKlHF7UdR+8KN/77QuKjlG45XPaBjz723edmfOWzxowRWfhjNbUDTJl6J/fe96g373/wMFSzj/cvCn/cNPG3L7p2ZcWmNq16k2V6/pvFpsDAAAAAAAAgJdk8PEf8JyFM1qLjjAmDDYpp6m+vrq+8UMX57xP/iBJMqmpfsDeJGlpHPz5vq7e6voP/vXeJMmZi6cP2Hegpy9NDXp0496sE5PZKytHWJ3z74tOAwAAAAAAAMBR8n/4YZgWvaCg1Nx48J/PvGmTcsOHLs7Hrjgls9qaD/sd86dNqq7/6E2nJqlMyvnOvU+ms/tgOefHm3YkSd55zqIsmF75zEm//+3c+djOl/4HYexbdUWy6cZk77aikwAAAAAAAABwlJRyYJi+/hvn9zvKq/kFU2vmT5uUX1q75EW/47O/dGZ1vWzW5CTJLRuezXs/d0fe9/k7Buz/vUtW5AOvO7l6/6a/vCF9feWjiU8tWXVFknJy/zeKTgIAAAAAAADAUVLKgWGa1tqUE49rr94fzVFSq+ZNyfd++4Lc/ZHXpvW5Y66en37z/fUDp6K0tzSmuaH/sVcbtu894p9LjZmzMpl5QuUIKwAAAAAAAABqklIOHKVSSkf1uROPa8+Ulsa0NFbKNt+658kj+vzO/d1H9XOpIaVSZVrOxuuTfc8UnQYAAAAAAACAo6CUA0do4YxJx+R7yoc5herXL1ze7/7MJdP73V//8Pb8022bj0kGxrCVlyfl3uSBbxWdBAAAAAAAAICjoJQDR+gr/+H8fPgNK3LScW0v6XtWHN8+6PMPvO7kfvez2pr73f/Pqx/K7/7LPenq6X1JP58xbu5pybTFyX1fLzoJAAAAAAAAAEehoegAUGtmtzfnPRcsH3rjEOrqSnngv74+5XLyhk9flw3b9+WUeVNSKpXy3d++IJOeO97qcPZ09qS57cX3UMNKpWTV5cnNn0k6diSTpg/9GQAAAAAAAADGDJNyoEDNDfVpaazP7PbKNJyLTp6TJDnpuPYsnNH6op/d3dE94vko2Ko3JX3dyQPfKToJAAAAAAAAAEdIKQfGgKaGyj/F1ubBJ99cvGLOgGe7O3tGNBNjwPwzkikLkvsdYQUAAAAAAABQa5RyYAwolUpJkrbmwU+U+8wvnJGr3//KtLccfL/LpJzxr1RKVr4xefj7SdeeotMAAAAAAAAAcASUcmAMuGfLziRJuTz4+6aGupwwpy29fQc3OL5qglh1RdLblTz4b0UnAQAAAAAAAOAIKOXAGLBy7pQkydrlM1903/4DvdX17s7uPLW7M5+9dkPKh2vzUPsWnpO0HZfc97WikwAAAAAAAABwBAY/KwcYVX/7rrPy+M6OLJ/dNuzPPLh1T/6/r96bJLngpNk5+fj2kYpHkerqKkdY/eTzyYF9SdPkohMBAAAAAAAAMAwm5cAY0NJYf0SFnCT5u5s2Vded3b0vspOat+qKpKcjefjqopMAAAAAAAAAMExKOVBD1v/R6/OxK04Z8Lynz/FV49qi85LWmY6wAgAAAAAAAKghSjlQQ1oa6/NLa5fk0jVz+z3v6jEpZ1yrb0hWXJY8+G9Jd2fRaQAAAAAAAAAYBqUcqEHfuvvJfvf7upRyxr1VlycH9iaP/KDoJAAAAAAAAAAMg1IOjAPfvvfJoTdR25a+MmmZ5ggrAAAAAAAAgBqhlAM16JrfubDf/VfueDwPb9tTTBhGR31jcvIlyYPfSXq7i04DAAAAAAAAwBCUcqAGLZk1ecCz7t5yv/uOA73p66s8u2XDM3l8Z8eoZGMErbgs6dyZbLqh6CQAAAAAAAAADEEpB2rU6085vt/9v9y+JUny5K6OLPnQt7LyD7+TP77q/iTJz/71zXnNn/1o1DNyjC2/OGmYlKz/VtFJAAAAAAAAABiCUg7UqP/2tjX57VefVL3/+l1PJEnWfuIH1Wf/9/pH87+veThJsv9A7+gG5Nhraq0Uc9Z/KymXh94PAAAAAAAAQGGUcqBGTWlpzG+9+sR84zdekSTZtqcrf/79hwbs+2/feWC0ozGSVl6W7H48eeInRScBAAAAAAAA4EUo5UCNW71ganX9Z9978EX3XvPAtpGOw0g76fVJqT5Z/82ikwAAAAAAAADwIpRyYAJ51/+7LT/e+GzRMXgpWmcki8+rHGEFAAAAAAAAwJillAMTzFs/c1N2d3bnczdvSrlcLjoOR2PFZcnT65PtD+NVERYAACAASURBVBedBAAAAAAAAIDDUMqBcaipvi4Xr5hz2Pfnf/IH+f1/vTcf+fpPRzEVx8yKSytXR1gBAAAAAAAAjFlKOTAONNaX+t0/+MeX5C/ecXpef8rxg+7f09mTJPm7mzaNeDZGwLSFydzTHGEFAAAAAAAAMIYp5cA48NHLTx3wbHJzQ1YvmJokmdbamDv/8DWjHYuRtOKNyZZbkz1bi04CAAAAAAAAwCCUcmAceOc5i7Lxk5dmyczW/NxZC6vPZ05uSpIc6OnLtNamzJvaUlREjrXnj7B64KpicwAAAAAAAAAwKKUcGEeu+cBF+eRb1lTvF85oTZLsP9CbJPnoFQMn6vT1lUcnHMfWnJXJ9KXJ/d8sOgkAAAAAAAAAg1DKgXFsVltzv/tXr5xTXb/rvCVJks6e3tGMxLFSKiUrL0sevTbp3FV0GgAAAAAAAABeQCkHxrHprY1JkslN9UmSUqlUfbdybnuS5ImdnaMfjGNjxWVJX3fy0PeKTgIAAAAAAADACzQUHQAYOXOmtOSvf/GMnL10RvXZp3/uZblj044sn92WJNmyY39OmNNWVEReigVnJZPnJOu/max+a9FpAAAAAAAAADiESTkwzr32lOMzrbWpen/Fy+bno1ecmuOntiRJPv39hwb93NZdnXlqtyk6Y1pdfXLyJZVJOT1dRacBAAAAAAAA4BBKOTBBzWmvlHJ+snnngHed3b059xPfzzkf//5ox+JIrXxjcmBvsuFHRScBAAAAAAAA4BBKOTBBNTVU/vm3Nw88xW7FH3xntONwtJZekDS1V46wAgAAAAAAAGDMUMqBCeySU4/Pnq6ebN11+GOqtu91LNKY1tCcnPia5IGrkr7eotMAAAAAAAAA8BylHJjA9h+olDje8lc3Vp89vad/CeeOTTv6vevrK49OOIZvxaXJvqeTLbcVnQQAAAAAAACA5yjlwAQ2d2pLkuTxnR35Xz94KMnAUs57/uH23Lzhmdz12M6c9cdX5+NX3T/qORnCia9J6hodYQUAAAAAAAAwhijlwAT22lOOq67/9LsPJkn2dHYnSd513pLquy/d9liu+MsbkiR/c/2joxeQ4WmZmix7ZXL/N5OySUYAAAAAAAAAY4FSDkxgF508Z8CzvV09SZI3nz6/+mzzs/tHLRNHacWlyY5Hk20mGQEAAAAAAACMBUo5MIGVSqXqelZbU5KDpZzJzQ3Vd3V1B/e985xFo5SOI3LypUlKjrACAAAAAAAAGCOUcmCCW7NgapJk5dwpSZIHtu5Jkkyd1JjTnnt366PPVvd/4ZbNo5yQYWk/LllwllIOAAAAAAAAwBihlAMT3JXvPru63tPZnf99zSNJKqWcr/3GK4qKxdFYeVny5F3JTsUpAAAAAAAAgKIp5cAEN2NyU85eOiNdPX25/qHt1edNDYf/9VAul0cjGkdqxWWV6/qris0BAAAAAAAAgFIOkDTV12V3R3fue3L3gHd/9vbTquufefn8JMnerp5Ry8YRmLk8mb3SEVYAAAAAAAAAY4BSDpCbNzyT9Vv35C9+8PCAd7Pamqvrs5fMSJJs2dExatk4QisuTTbdmOx/tugkAAAAAAAAABOaUg6QdSfO6nf/zd98RXXd/NwxVi9fNC2bn92fJPnkt9ePXjiOzIpLk3Jv8uB3ik4CAAAAAAAAMKEp5QBZd+LsfvdNDQd/NUyf3JQkOXvpzPz8uYuTJItmtI5eOI7MvNOTKfOT+x1hBQAAAAAAAFCkhqIDAMVrrC/1u5/TfvDIqpOOa8/X3nd+Tpk3JQ31dWmqr8vkZr86xqxSqTIt546/Tw7sS5omF50IAAAAAAAAYEIyKQdIe0tjv/tprU397k9bOC0N9ZVfFy2Ndens7h21bByFVVckPZ3JA98uOgkAAAAAAADAhKWUA+Ty0+blUz972rD27u7syZU3bkxfX7n67H/94KH8zpfvSndv30hF5EgsOi+ZsiC5+0tFJwEAAAAAAACYsJxBA6SurpQ3n74gy2a1ZfoLpuQczs6O7syY3JR7tuzKn373wSTJ289cmLOXzqju6e0r59//w4/znguW93vOCKurS1a/JbnpL5N9zySTZxadCAAAAAAAAGDCMSkHqDpt4bQsmtk6rL27Orr7XZNk257Ofnue2NmRq+/flrf/n5uOXUiGZ/Xbk76e5KdfKToJAAAAAAAAwISklAMckT9606lJkq6e3iTpd2TV4zs6kiTlcjmPbt+X3Z0HCzu9zx131d3bl71dPaMVd+I6/tRkzqrkni8XnQQAAAAAAABgQlLKAY7I/GktSZJL//z6JMnmZ/dX333i2+uTJP9422O56E+vyfUPba++e3pPV5LkV668Laf+l38brbgT2+q3Jo/dkuzYWHQSAAAAAAAAgAlHKQc4Ins6K1NuevvKuffxXfkvX/9pv/dfvHVzbt+0I0nybz/dWn3+5K6OdPf25brnijrlcnmUEk9gq99WuZqWAwAAAAAAADDqlHKAI7J22czq+rK/uH7A+w9/9Z48tG1vkuSOzTurzx95el+ueeDp6n1nd9+Az3KMTVuULFqb3P3lRAkKAAAAAAAAYFQ1FB0AqC2z2ppf9H25nNz12M4Bz3/ny3elueFgD3B3Z3cmNdUf83y8wOq3Jd96f7L1nmTumqLTAAAAAAAAAEwYJuUAR6SurnTUn+3qOTgd52+u25AbH9l+LCLxYk55c1LXkNzzpaKTAAAAAAAAAEwoSjnAMTW9tTELZ0wact9nr3s07/zsLaOQaIJrnZGc8Jrknn9J+nqLTgMAAAAAAAAwYSjlAEfsNy8+YcCzL/7auZnV1pTu3nI6u/sG+RSFWfO2ZM8TyaYbik4CAAAAAAAAMGEo5QBH7D+/9uR+9//83rVZu3xmfnntkuzt6snuju7qu+t/96I8+F8vGe2IHOqkS5KmtuRuR1gBAAAAAAAAjBalHOAlec8Fy3LmkhlJkpltzUmSrp6Dk3IWTG9NU0Nd5k+rHGn1GxcNnLLDCGtqTVa+Mbnv60l3Z9FpAAAAAAAAACaEhqIDALXpm7/5itSVSlk1b0r12Qlz2qrrly2clje9bF71vqG+lCQ5Zd6UfPaXzsyv/f2PRy8syeq3JXd9MXnou8mqy4tOAwAAAAAAADDumZQDHJVT50/tV8hJktntzdX1G1Yfn3edv7R6X19XKeU01tdlxuTG6vOe3oNTdRhBS1+ZTJ6T3OMIKwAAAAAAAIDRoJQDHDOtTfXVdUtjfb93Dc+VchrqS/3e7e3qGZ1wE119Q3LqW5IH/y3p2Fl0GgAAAAAAAIBxTykHOGb6lXIa+pdyHnxqb5Kkr1zOqrlTsnz25CTJ7g6lnFGz5m1J74Hk/q8XnQQAAAAAAABg3FPKAY6Z1qaG6vr4qS2D7nl2X3dKpVI++PoVSZLdnd2H/b7bN+3I03u6jm3IiWzey5MZy5O7HWEFAAAAAAAAMNKUcoBjpv65I6qSZOmsyf3e/czp85MkPb19SZIpLY1JXryU85a/ujGXfPraYx1z4iqVkjVvTzZen+x+oug0AAAAAAAAAOOaUg4wIuZNm9Tv/v2vPSlrl83MJavnJkmmTKpM1dnw9L4X/Z7tew+MTMCJavXbkpSTe/+l6CQAAAAAAAAA45pSDnBMXf3+V+aGD13cb2pOkiyY3povvufcTJ1UmZDz/KSc3//Xe6vTcw5VLpdHPuxENHN5Mv8MR1gBAAAAAAAAjDClHOCYOmFOW+a/YErOYKa2NlbXuzoGHmHV06eUM2JWvz3Zenfy9ANFJwEAAAAAAAAYt5RygEI8PyknSXbsH1jK6T5kes7uzoHveQlO/ZmkVGdaDgAAAAAAAMAIUsoBCvP3v3J2kmTH/gMD3nX3HJyUc9djO0ct04TQNidZdmFyz5cTx4QBAAAAAAAAjAilHKAw01ubkiQ79g0s5Wzf11Vdd3b3DXjPS7T67cnOTcljtxadBAAAAAAAAGBcUsoBCjN9cuUIq/f8w+0D3v3eV+6prm94ePuoZZowVl6WNExK7nGEFQAAAAAAAMBIUMoBCjNzcvNh39366LPV9ZU3bkzZMUvHVnN7cvIlyU+/mvR2F50GAAAAAAAAYNxRygEKM6mpPqfOn5J5U1uqz7bs2J8bHxk4Gectf3XjaEabGNa8Pdn/TPLID4pOAgAAAAAAADDuKOUAhVqzYFq6evqq96/91LV552dvGbDvjs07RzPWxLD8VUnz1GT9t4pOAgAAAAAAADDuKOUAhWprbsi+Az3V+/0Hevu9XzB90mhHmjgampLF5yUbrys6CQAAAAAAAMC4o5QDFGpyU0M6u/vy5K6OAe/eeNq8fPzNqwtINYEsXZc8uyHZ9XjRSQAAAAAAAADGFaUcoFCTm+uTJGs/8YMB7977ymWZ1tpYvS+Xy6OWa8JYsq5yNS0HAAAAAAAA4JhSygEKNampvrru6+tfumltasjUSQdLOf/n2g2jlmvCOO7UZNL05FGlHAAAAAAAAIBjSSkHKFRzw8FSzjP7DvR719RQl9amhur9J7+93rScY62uLll8frLx2qKTAAAAAAAAAIwrSjlAoV6z8rjq+qdP7Or3bkpLQ1oa+/+auv/JPaOSa0JZekGyc3OyY1PRSQAAAAAAAADGDaUcoFBTWxvz8+csSpK86//dVn3+vouWp72lMZMa6/vtf8OfO2bpmFuyrnLd6L8tAAAAAAAAwLGilAMUbuqkxn73N//eq/KB161IkjTU1+XvfuXsImJNHHNWJq2zkkeVcgAAAAAAAACOFaUcoHA/fWJ3v/v2loZ+9688aXZOmNM2mpEmllIpWfKKyqSccrnoNAAAAAAAAADjglIOULi3nrGg331rU/2APZ/5hZdX153dvSOeacJZui7Z/Xjy7IaikwAAAAAAAACMC0o5QOHmtDf3uy+VSgP2nDCnvbre1dE94pkmnCUXVK4bHWEFAAAAAAAAcCw0DL0FYGSdtnBafv3C5fm5sxaltXnglJwX2rm/O8dNaRmFZBPIrBOTtuOSR69LznhX0WkAAAAAAAAAap5JOUDhWhrr88HXr8iima2Z1dZ82H1//o7TkyQ79h8YrWgTR6mULFlXmZRTLhedBgAAAAAAAKDmKeUANeOE2W1Jkh37lHJGxNJ1yd6nku0PFZ0EAAAAAAAAoOYp5QA1Y2ZbU5LkP3z+joKTjFNL1lWuG68tNgcAAAAAAADAOKCUA9SMqZMaq+vu3r4Ck4xTM5Yl7fOSR68rOgkAAAAAAABAzVPKAWpGc8PBX1m3bXy2wCTjVKlUOcJq4/VJuVx0GgAAAAAAAICappQD1IxSqVRdv/OztxSYZBxbsi7Zvz3Zdn/RSQAAAAAAAABqmlIOMK595Os/zZIPfStdPb1FR6kNS9dVrhuvLzYHAAAAAAAAQI1TygFqyq0fflWSZO7UlmHtv/LGjUmSL/14y0hFGl+mL0mmLko2Xlt0EgAAAAAAAICappQD1JQ5U1pyxcvm5cldnenrKw/7cx0HekYw1TizdF1lUk5fX9FJAAAAAAAAAGqWUg5Qc7525xNJkm/c/cSL7rv38V3V9cevWj+imcaVJeuSjh3Jtp8WnQQAAAAAAACgZinlADVry46OF31/3xO7q+t5wzzuilQm5STJo9cVmwMAAAAAAACghinlADWnqaHyq2tXR/eL7uvs6a2un9jVOaKZxpWpC5LpS5ONSjkAAAAAAAAAR0spB6g5f/2LZyRJ7ty8M3/x/YcOu29PZ0+/+w1P780/3ro5v/y3t45ovnFh6bpk4w1JX+/QewEAAAAAAAAYoKHoAABH6sKT56StuSG3bnw2t258Nj9/7uLMmNw0YN/373+q3/3F/+NHoxWx9i25ILnj75OtdyfzTi86DQAAAAAAAEDNMSkHqEl1pYPrv/zhwwPedxzozR2bdx728+VyeSRijR9L11WujzrCCgAAAAAAAOBoKOUANWn3IUdTLZg+acD7rbs7q+uzl84Y8P6mDc+MTLDxov34ZOaJyUalHAAAAAAAAICjoZQD1LyPfuO+LPnQt7Kns7v6bO9zpZ0/uGxVnt13YMBnmur9+hvS0nXJppuS3p6h9wIAAAAAAADQj/8rDdSk9120fMCzx3d2VNedPb1JkpOOa8vLFk4bsLdBKWdoS9YlB/YkT95ZdBIAAAAAAACAmuP/SgM1qa25ccCzjgO9A9aTGuvziZ9ZPWBvT2/fyIUbL5asq1wfvbbYHAAAAAAAAAA1SCkHqEltLQ0Dnu0/tJTTXVm3NNansb4uF6+Y02/vVfdsHdmA40Hb7GT2ymTjdUUnAQAAAAAAAKg5SjlATZo2aeCknL1dPUmScrmcP//+Q0mSSU31SZKPXn5Kv73/dNvmEU44Tixdl2y+Oek5UHQSAAAAAAAAgJqilAPUpFltzQOe7T/Qk3K5nKW/d1V++sTuJJXjq5JkwfRJWTl3Sn771SclSS5dM3f0wtayJa9IuvcnT9xRdBIAAAAAAACAmqKUA9Skly+elnedtyQfeN3J+ZO3rE6S7O3qTVdPX799x01pSZKUSqV8+7fW5bdefWKS5Es/3pKn93SNbuhatPgVleujjrACAAAAAAAAOBJKOUBNam6oz0cuPyXvu+iEXLZmXpLkD/713nR29/bbV19XOux3nPXHV49oxnFh8szkuFOTjdcWnQQAAAAAAACgpijlADXv+SOqkuRlH/tedX3Dhy4edP9vXnxCdd3V0zvoHg6xZF3y2K1Jj8lCAAAAAAAAAMOllAPUvLpBpuF84HUnZ/60SYPuP2FOW3X9z7dvGbFc48bSdUlPZ7LltqKTAAAAAAAAANQMpRxgXPj2b63rd796/tTD7r38tHnVdccBk3KGtPi8JKVk4/VFJwEAAAAAAACoGUo5wLiwcu6UfvfTW5sOu7dUKuX8E2YmSa6658kRzTUuTJqezF2TPHpd0UkAAAAAAAAAaoZSDjAuHXpE1WD++hfPTJLcsXlnenr7RiNSbVuyLtlya9LdWXQSAAAAAAAAgJqglAOMO5/71XMyqan+Rfe0HvJ+Z0f3SEeqfYvPS3oPJE/8pOgkAAAAAAAAADWhoegAAMfKF37tnKx/ck9eceKsIfeWSqXqeuf+A5nV1jyS0WrfwnMr1803JovXFpsFAAAAAAAAoAYo5QDjxnnLZ+W85UMXcl5ox36TcoY0eWYy6+Rk881FJwEAAAAAAACoCY6vAiasL/y7c5IkO/YdKDhJjVh0brL5lqSvt+gkAAAAAAAAAGOeUg4wYS2c0Zok2dVhUs6wLD4v6dqVbLuv6CQAAAAAAAAAY55SDjBhTWqqT5J0dpv8MiyL1laujrACAAAAAAAAGJJSDjBhTWqslHL2H1DKGZZpi5L2ecmmG4tOAgAAAAAAADDmKeUAE5ZSzhEqlZLFa5PNNyXlctFpAAAAAAAAAMY0pRxgwqqrK6W5oc7xVUdi0dpkz5PJzk1FJwEAAAAAAAAY05RygAmttanepJwjsWht5brppmJzAAAAAAAAAIxxSjnAhNba1KCUcyTmrEpaplaOsAIAAAAAAADgsJRygAltcnN99nX1FB2jdtTVJQvPVcoBAAAAAAAAGIJSDjChtTY1ZN8BpZwjsujcZPuDyb7tRScBAAAAAAAAGLOUcoAJbf3W3bnuIeWSI7L4vMp1883F5gAAAAAAAAAYw5RygAmts7svSfKjB58uOEkNmXd6Ut/sCCsAAAAAAACAF6GUA0xor111XJLkV6+8LZ+++qGc9tHvplwuF5xqjGtoTuafkWy6segkAAAAAAAAAGOWUg4woXX2VCbl1NWV8qmrH8yuju78w82bCk5VAxavTZ68Kzmwr+gkAAAAAAAAAGOSUg4wof3cWQuTJAeeK+ckyWPP7i8qTu1YtDYp9yZbbis6CQAAAAAAAMCYpJQDTGhvWD03rzhhVr9nn73u0YLS1JCFZycpJZtuKjoJAAAAAAAAwJiklANMeOtOnDX0JvprmZocf2qyWSkHAAAAAAAAYDBKOcCEt2LulKIj1KZFayvHV/V2F50EAAAAAAAAYMxRygEmvBmtTQOelcvlApLUmEVrk+79yda7i04CAAAAAAAAMOYo5QAT3qSm+gHPPnfzpgKS1JhFayvXTY6wAgAAAAAAAHghpRxgwmtuOPircN2Js5Ikd2/ZVVSc2jFlbjJ9SbJZKQcAAAAAAADghZRygAmvpfHgpJyPv3l1kmTNwmlJkqf3dOWrP9mSr9/1RG565JlC8o1pi86rlHIc9wUAAAAAAADQT0PRAQCKNru9OR9+w4okyYzJTUmSrbs6kiTv/dztuX3TjureDR9/Q+rqSqMfcqxadG5y1xeS7Q8ls08qOg0AAAAAAADAmKGUA5DkPRcsT5L09VUmvvzlDx/Jwumtueuxnf32LfvwVdn4yUtHPd+Ytfi8ynXzjUo5AAAAAAAAAIdwfBXAIQ6dgvOhr9yTnr6BxzL1DfJswpp5QtI6K9l8c9FJAAAAAAAAAMYUpRyAI7RlR0fREcaOUqlyhNWmG4tOAgAAAAAAADCmKOUAvMAlpx7/ou8f27F/lJLUiMXnJTs3JbufKDoJAAAAAAAAwJihlAPwAn/1C2fk5YumHfb99Q9vH8U0NWDR2sp1803F5gAAAAAAAAAYQ5RyAAYxubmhuv7Dy1YlSX7m9PlJkr+65pFCMo1Zx69JGicnm5RyAAAAAAAAAJ7XMPQWgIntV16xNO8+f0lKpVK+8pPH8wvnLio60thS35AsPCvZfHPRSQAAAAAAAADGDJNyAAbRVy73uy+VSkmSeVNb0tndV0SksW3R2uSpe5OOnUUnAQAAAAAAABgTlHIABtHbVynlrFkwtd/ztpaG7OvqKSLS2LZobZJy8titRScBAAAAAAAAGBOUcgAG0ffcMJwPvm5Fv+dtzQ3Zq5Qz0IIzk7qGZPNNRScBAAAAAAAAGBOUcgAG8fzxVU0N/X9NtrU05rqHtqf8guOtXszdW3bmV668Ld294/jYq6bJydzTlHIAAAAAAAAAnqOUAzCIaa2NSZKWxv6/Jq998Okkyedu3pQkuf/J3Xnk6b0v+l2X/68b8oP127LpmX0jkHQMWbQ2efz2pLuz6CQAAAAAAAAAhVPKARjEn7xlTX7/0pVZPX/qoO+/cdeTSZJLPn1dXvU/fjSs77z6/m3HLN+YtPi8pPdA8sRPik4CAAAAAMD/z96dRll6FvaB/9/at963ql61ob27S1I3WoAICGZ14tg+9rEH4pA5xmPPOcTJGDg4JI7jMcQxM4fgEzs4ntieJBPA2GYYMDYJIBAggdZWS2gXUi/qqt7UW3V17Xc+XCip3C3obt1737r3/n5fnud979Xz/KUPdT/of54HACicUg7AOawa6M4vvu6ylEqlc35+z3MvZHbuR19h9dIrqz51z96q5VuUNt1SGffeVWwOAAAAAAAAgEVAKQfgAjz+v791fn75P//igs/+avdIfuLffzNzLynr7Np3fH7+Y9euq33AIvWvSlZfley5u+gkAAAAAAAAAIXrKDoAQCPp6Ww/5/tf/dSD+dyuA0mSrz91OG+4am2S5Gc+8WJBZaC7s/YBi7b5luS7/28yN5u0nfu/FQAAAAAAAEArcFIOQBX8oJCTJMfHp875ncmZ2XrFKc6W25LJE8mhR4tOAgAAAAAAAFAopRyAC/RTN2z4oZ+fnqyUb/YeHV/wfmJ6rmaZFo3Nt1ZGV1gBAAAAAAAALU4pB+AC3fnUkR/6+Zmp2Tx96FT+zkfvmH+3vK+zNU7KWb45Wboh2XtX0UkAAAAAAAAACqWUA3CBPvPLt6a3s/2s9yv7u5Ikp6dmsveFhafk9Hd1tMZJOaVS5QqrPXcl5XLRaQAAAAAAAAAKo5QDcIEuXd2fR/71W/ITw+vzzps3z79/y3WDSZL/+u09OTI2teCf6e5oy188sD97jp6ua9ZCbL41GTuYvPC9opMAAAAAAAAAFEYpB+AitLeV8vGfuyHvvHlLkuRfvOOa/NZPXJckOTI2lf9v14H5737kJ7fme0cqZZzbP/q1umetuy23Vca9dxebAwAAAAAAAKBASjkAr8C165fmsd96a37xdZels/3FP6nffPrI/PynbtxQRLTirL4q6V1ZucIKAAAAAAAAoEUp5QC8Qr1d7fPzH982lEtX988/P/c770hP54uf97/ku02rra1yhZVSDgAAAAAAANDClHIAqmiguyPPfv+qqpu2rJh/f8XagSTJ6anZ7Np3vJBsdbXl1uTYs8nJkaKTAAAAAAAAABRCKQegiga6O+bn9+85Nj//b7948/z8sw/sr2umQmy5rTLudVoOAAAAAAAA0JqUcgCqqPyS+a+8/vL5+dqlPfnwT16fJPnkvfvqnKoAg9uTzv5kz91FJwEAAAAAAAAohFIOQBWdnpyZn/+zN1254LOrB5cmSaZm5pr/Cqv2jmTTq5O9SjkAAAAAAABAa1LKAaiiD73jmiTJ9RuWpqtj4Z/YrRuWzc+/+vihuuYqxJbbkoPfTc4c+9HfBQAAAAAAAGgySjkAVbSkpzOf+eVb83//41ef9dlLSzq/95Wn6hmrGJtvTVJO9n6n6CQAAAAAAAAAdaeUA1BlOy9ZmVUD3T/0Oyv6OuuUpkAbdyRtncneu4pOAgAAAAAAAFB3SjkABSgXHaAeOnuTDTcme5RyAAAAAAAAgNajlANQR2+6Zl2S5Pj4dE5OTBecpg623JYceDCZGi86CQAAAAAAAEBdKeUA1NF/eNeNuWHz8iTJ0bGpgtPUwebbkrmZZP+9RScBAAAAAAAAqCulHIA66mxvy6/cfnmS5PTkTMFp6mDTq5OUkr13F50EAAAAAAAAoK6UcgDqbKC7I0ky1gqlnN7lyeD1yZ67ik4CAAAAAAAAUFdKOQB1NtBTKeW0xEk5SeUKq/33JrPTRScBAAAAAAAAqBulHIA66//+STmPj54qOEmdOtLiTwAAIABJREFUbLk1mR5PRh4qOgkAAAAAAABA3SjlANTZD66v+uiXnig4SZ1svq0yusIKAAAAAAAAaCFKOQB19oNSTpJ89sH9BSapkyXrkpWXK+UAAAAAAAAALUUpB6DO+rra5+fv/8zuApPU0Zbbkr13J3NzRScBAAAAAAAAqAulHIA6K5VK8/OZuXKBSepoy23JxPHk8ONFJwEAAAAAAACoC6UcgAJ8+pdumZ8fPjVZYJI62XxrZdzzrWJzAAAAAAAAANSJUg5AAW6+bFXedcvmJMnOD3+54DR1sOKSZMn6yhVWAAAAAAAAAC1AKQegIEPLeufn+14YLzBJHZRKyZZbkz13JeUWubILAAAAAAAAaGlKOQAFeem1Vb/3lacKTFInm29NTo0kx54rOgkAAAAAAABAzSnlABTkpaWcdUt7CkxSJ1teUxldYQUAAAAAAAC0AKUcgKKUXpz++zuezszsXHFZ6mHN1UnP8mTPt4pOAgAAAAAAAFBzSjkABbn1slULnq/40F9ndq5cUJo6aGtLttyW7HFSDgAAAAAAAND8al7KKZVK7aVS6cFSqfSFWu8F0EjeefPm3PuhNy1496HPPlxQmjrZfGvywjPJqYNFJwEAAAAAAACoqXqclPOrSR6rwz4ADaVUKmXNku584K1Xzb/71L37CkxUB1tuq4x77yo2BwAAAAAAAECN1bSUUyqVNiZ5R5L/q5b7ADSy97zusgXP41MzBSWpg6HtSWefK6wAAAAAAACAplfrk3L+XZIPJJmr8T4ADauzvS0P/as3zz9/YfdIgWlqrL0z2bjTSTkAAAAAAABA06tZKadUKv14kkPlcvn+H/G9XyqVSveVSqX7Dh8+XKs4AIvast7O+flvfO6RApPUwZbbktFHkjPHi04CAAAAAAAAUDO1PCnnNUn+fqlUei7Jp5K8sVQq/de//aVyufwfy+XyjnK5vGPNmjU1jAPQGGZmy0VHqK0ttyUpJ/vuKToJAAAAAAAAQM3UrJRTLpd/vVwubyyXy5ck+bkkXy2Xy++q1X4Aje6x33prkuRdt2yZf1cul/PqD38533zqSFGxqm/DjqStM9nzraKTAAAAAAAAANRMLU/KAeAC9Ha1Z0lPx4J3X3x4NIdOTeZd/+k7BaWqga6+ZP0Nyd67i04CAAAAAAAAUDN1KeWUy+WvlcvlH6/HXgCNbKC7I39613O55IN/lX0vjOfA8TNFR6qNLbcmzz+QTDfpvx8AAAAAAADQ8pyUA7CI9He/eFLO1544tOC5qWy+LZmbTvbfV3QSAAAAAAAAgJpQygFYRJ4+NDY//5ef+24eOXBi/vno2GQRkWpj881JSq6wAgAAAAAAAJqWUg7AInbo5ItFnJt++8uZnSsXmKaKelck665L9txVdBIAAAAAAACAmlDKAVhEvvhPXrfgeXxqJqv6u+afJ2dm6x2pdja9Onn+/mRurugkAAAAAAAAAFWnlAOwiFy7fmm+8YE3zD+fnpzJdRuWzT9PTDdRgWXDjmTyZHL0qaKTAAAAAAAAAFSdUg7AIrNpZd/8/KH9J3JifGr+ualOytm4szLuv7fYHAAAAAAAAAA1oJQDsAhtfcnpOA/tPzE/b6qTclZdkfQsU8oBAAAAAAAAmpJSDsAitKK/a8Hzx39uOEmTnZTT1la5wmr/fUUnAQAAAAAAAKg6pRyARejGzcsXPC/p6UjSZCflJMnGHcmhR5PJsaKTAAAAAAAAAFSVUg7AIvSrf/dV8/Pf+/kb0tPRniR56uCpoiLVxsadSXkuOfBg0UkAAAAAAAAAqkopB2ARKpVK8/O/v3195sqV+fv/fHdBiWpkw02Vcf+9xeYAAAAAAAAAqLKOogMAcG4P/cabMzVbua5qeV9nwWlqpG9lsuqKZP99RScBAAAAAAAAqCqlHIBFatlLijjXb1iWJNmxZUVRcWpnw47kma8m5XLykhOCAAAAAAAAABqZ66sAGsTrr1ozf3JOU9m4Izl9KDmxr+gkAAAAAAAAAFWjlAPQIA6dnMzu/SfyqXv2Fh2lujburIz77y02BwAAAAAAAEAVKeUANIhHR04mST74lw8XnKTK1l2XdPQm++8rOgkAAAAAAABA1SjlADSIy9f0z8+PjE2mXC4XmKaK2juT9Tc4KQcAAAAAAABoKko5AA3iz3/5tvn5jt/+cv78/v0Zm5wpMFEVbbwpGdmdzEwWnQQAAAAAAACgKpRyABrEiv6u/LM3XTn//IG/2J3r/9WX8rOfuDsT07MFJquCjTuT2clk9JGikwAAAAAAAABUhVIOQAP5zP375uc/uL3qnudeyDeeOlJQoirZuLMyusIKAAAAAAAAaBJKOQAN5P1vueqc76dn5+qcpMqWrk+WblDKAQAAAAAAAJqGUg5AA3nV2iXnfP/pe/ed831D2XBT8vx9RacAAAAAAAAAqAqlHIAGsqSn45zvv/7k4Tw+erLOaaps487k2HPJ2OGikwAAAAAAAAC8Yko5AA1kaFlP3nb94Dk/e+rgWJ3TVNnGnZXRaTkAAAAAAABAE1DKAWggHe1t+Q/vuumcn52enKlzmiob2p60dST77y06CQAAAAAAAMArppQD0MA+8a4b5+ffeuZogUmqoKsvWXedUg4AAAAAAADQFJRyABrY669am6+97/VJks8/dKDYMNWwcWfy/IPJ3GzRSQAAAAAAAABeEaUcgAbW3dGWS1b3J0nedv1gwWmqYOPOZOpUcviJopMAAAAAAAAAvCIdRQcA4MJ9+pduyXcPnEypVJp/99ePjBaYqEo27qyM++9N1l1bbBYAAAAAAACAV8BJOQAN6ObLVuV/fu2lRceovpWXJb0rKqUcAAAAAAAAgAamlAPQBH7h1i1Z1tuZJHny4Kl87YlDBSe6SKVSsmFH8vz9RScBAAAAAAAAeEWUcgCawIq+rpw4M519L4znzR+7M+/+kwY+aWbjzuTQY8nEyaKTAAAAAAAAAFw0pRyAJvCDU3Le/LE7C05SBRt3JCknBx4oOgkAAAAAAADARVPKAWgCy/sqpZwz07Pz7+bmykXFeWU23FQZ9zfwaT8AAAAAAABAy1PKAWgCl6zuP+vd6amZApJUQe/yZPWVyf77ik4CAAAAAAAAcNGUcgCawI2bV5z17tTETD76pcdz4PiZAhK9Qht3Vko55QY97QcAAAAAAABoeUo5AE3q3335yfz+Hc/ktt/5atFRLtzGHcn4keTYc0UnAQAAAAAAALgoSjkATerP7ts/P5+ba7ATZzburIyusAIAAAAAAAAalFIOQJO4cfPyl/3sk/furWOSKlhzTdLZl+y/t+gkAAAAAAAAABelo+gAAFTHX/zKbdl/7Ey6Otpy80e+suCzD332kbzz5i0FJbsI7R3J+huT552UAwAAAAAAADQmJ+UANIlSqZRNK/uybmlPfventyVJXrV2IEky0N2AHcyNO5KR3cn0RNFJAAAAAAAAAC6YUg5AE7rpkhVJkn/x49fmp27ckLHJmYITXYSNO5O56WR0d9FJAAAAAAAAAC6YUg5AE7p8zUC+95G35/Yr1+SOxw8lSe565kjBqS7Qxh2Vcf+9xeYAAAAAAAAAuAhKOQBNqq2tlCSZmSsnSfYeHS8yzoVbMpgs26SUAwAAAAAAADQkpRyAJvf7/9ONSZKV/V0FJ7kIG3ck++8vOgUAAAAAAADABVPKAWhyG1b0JknueuZoRk6cKTjNBdq4MzmxNzk1WnQSAAAAAAAAgAuilAPQ5Pq62pMkf3rXc3nN73y14DQXaOPOyrj/vmJzAAAAAAAAAFwgpRyAJtfb2T4/nysXGORiDG5L2jqT/fcWnQQAAAAAAADggijlADS5pT2dC56fOniqoCQXobMnGdyaPH9/0UkAAAAAAAAALohSDkCTa2srLXj+sY/dmYnp2YLSXISNO5PnH0hmZ4pOAgAAAAAAAHDelHIAWtDXnjhUdITzt3FHMn06Ofx40UkAAAAAAAAAzptSDkAL6mxvoD//Q8OVcWRXsTkAAAAAAAAALkAD/V9ZAC7W19//+nz9/a/PT9+4MUlyaqKBroJadUXSNZAcUMoBAAAAAAAAGodSDkAL2LKqP1tW9efX3351kuSBvccKTnQB2tqSwW1OygEAAAAAAAAailIOQAtZ2tOZJPnPd+8pOMkFWj+cjD6SzDbQCT8AAAAAAABAS1PKAWghXR0N+md/aDiZOZMceaLoJAAAAAAAAADnpaPoAADU144tK4qOcOHWD1fGA7uSddcVmwUAAAAAAADgPDTokQkAXKxlvZ05Mz1bdIwLs+qKpLM/GdlVdBIAAAAAAACA86KUA9Bi+ro7Mj7VYKWctvZkaFvlpBwAAAAAAACABqCUA9Bi+rvaMz41U3SMCzc0nIw+nMw1WKEIAAAAAAAAaElKOQAtpq+rI+OTDVhsWT+czJxJjjxZdBIAAAAAAACAH0kpB6DF9He35/TUTMrlctFRLszQcGV0hRUAAAAAAADQAJRyAFpMT2d75srJ1Oxc0VEuzOpXJZ39yYhSDgAAAAAAALD4KeUAtJiezvYkyYHjE7n7maMFp7kAbe3J4FYn5QAAAAAAAAANQSkHoMX0fr+U88b/82v5+T/6dmMVc9YPJ6O7k7nZopMAAAAAAAAA/FBKOQAtZsclK5Ik5XLl+e5njuSPv/lsTk1MF5jqPA0NJ9PjyZGnik4CAAAAAAAA8EMp5QC0mA3Lexc8f/2pI/mtLzyad//JvQUlugDrhyvjiCusAAAAAAAAgMVNKQegxfR1taer/cU//987NJYkuX/PsYxPzRQV6/ysvjLp7EsOKOUAAAAAAAAAi5tSDkCLKZVKWdbXOf98avLFIs7zx84UEen8tbUng1udlAMAAAAAAAAseko5AC1oeW/nOd/vX+ylnCQZGk5Gdidzs0UnAQAAAAAAAHhZSjkALWhwWc853+85errOSS7C0PZk+nRy9OmikwAAAAAAAAC8LKUcgBY0NTN3zvcf/uJjdU5yEdYPV8YDrrACAAAAAAAAFi+lHIAWdN+eY+d8Pz1bTrlcrnOaC7T6qqSjNxlRygEAAAAAAAAWL6UcgBb0K7df/rKfnTwzU8ckF6G9Ixm83kk5AAAAAAAAwKLWUXQAAOrv1958Za5YO5AV/V25ct1AZufK+dj/eCp/8cD+vOljX8/hU5N59t+8PaVSqeio5zY0nDz0yWRuLmnTLwUAAAAAAAAWH/8nE6AFlUql/IMbNuT2K9dkaFlvNq7oy9u3DiZJDp+aTJKMT80WGfGHWz+cTI0lR58uOgkAAAAAAADAOSnlAJAkWd7XteD51MQivsZqaLgyjrjCCgAAAAAAAFiclHIASJK8at3AguexyemCkpyHNVcnHT3JAaUcAAAAAAAAYHFSygEgSbK0p3PB88nFfFJOe0ey7non5QAAAAAAAACLllIOAPO2blg2Px9bzKWcJFk/nIzsTubmik4CAAAAAAAAcBalHADmff69r82X/unfSZKcnFjE11clydBwMnUqeeF7RScBAAAAAAAAOItSDgALrOzvSpIcHZsqOMmPsH64MrrCCgAAAAAAAFiElHIAWGBVf1fa20r5xlNH8mf37Ss6zstbc3XS3p0ceLDoJAAAAAAAAABnUcoBYIG2tlLWDHTny48dzAf+fHdm58pFRzq39s5k8Ppk5KGikwAAAAAAAACcRSkHgLOsXdo9Pz98ajIT07P5/EMHCkz0MoaGK6WcubmikwAAAAAAAAAsoJQDwFk621/8efjm00fyvs88lPd+8sE8uPdYganOYf1wMnkyOfZs0UkAAAAAAAAAFugoOgAAi88Lp6fm5+/7zIvXQy26q6yGhivjgQeTVZcXmwUAAAAAAADgJZyUA8BZ1i/veXG+7MX59OwiK+WsvSZp705GdhWdBAAAAAAAAGABpRwAzvLxn7shf/gPb8qG5b3ZuKJv/v2Z6ZkCU51De2ey7rrkgFIOAAAAAAAAsLgo5QBwltUD3XnLdYPp727PPc+9MP9+fGq2wFQvY/1wMrI7KS+yU3wAAAAAAACAlqaUA8DL6u3qWPA8PrkISzlD25PJE8kL3ys6CQAAAAAAAMA8pRwAXtZD+44veP7wFx8rKMkPMTRcGUdcYQUAAAAAAAAsHko5ALys9rbSgucTZ6ZTXmzXRK29NmnvSg4o5QAAAAAAAACLh1IOAC+rvVQ6693hU5MFJPkhOroqxRwn5QAAAAAAAACLiFIOAC/rj/7RjlwztDR/9U9em9/96W1Jkt/43HcLTnUO64eTkYeSxXaKDwAAAAAAANCyOooOAMDidfuVa3L7lWuSJGuX9CRJdu07XmSkcxsaTu7/0+TYs8nKy4pOAwAAAAAAAOCkHADOz5ol3bl8TX9u3LK86ChnWz9cGQ+4wgoAAAAAAABYHJRyADhvq/q7c3RsqugYZ1t7bdLWmYwo5QAAAAAAAACLg1IOAOdtZX9Xdu07nl//y4eza9/xHDw5UXSkio7uZN21TsoBAAAAAAAAFo2OogMA0DhWDXRlcmYun7xnbz55z94kybc++MZsWN5bcLIkQ8PJo59LyuWkVCo6DQAAAAAAANDinJQDwHlb1d911rs/uOPpApKcw/rhZOJ4cnxP0UkAAAAAAAAAlHIAOH/L+s4u5aw4x7tCDA1XRldYAQAAAAAAAIuAUg4A523XvuMLnjvaSpktlwtK87esuy5p60xGlHIAAAAAAACA4inlAHDe/uU7rlnwvHqgO0dOTRaU5m/p6E7WXJ2MPlx0EgAAAAAAAAClHADO39qlPXnyt9+WJFnR15nVS7pyZGyRlHKSZGhbMrK76BQAAAAAAAAASjkAXJiujrY89eG35Z4PvalyUs7YVNGRXjS4NTl9KDl1sOgkAAAAAAAAQItTygHggnW2t6WzvS2rB7pz+NRkTk1MZ26uXHSsSiknSUadlgMAAAAAAAAUSykHgIu2eqA7oycnsvU3/3te97t3FB1HKQcAAAAAAABYNJRyALhoqwe65ufPHz9TYJLv61mWLN+SjD5cdBIAAAAAAACgxSnlAHDR1izpLjrC2Qa3JiNOygEAAAAAAACKpZQDwEVb1tu54Hlmdq6gJC8xtD154XvJ5KmikwAAAAAAAAAtTCkHgIt2+ZqBBc/fefaFgpK8xODWJOXk4KNFJwEAAAAAAABamFIOABdt08q+PP3ht+UP3nljkuTOpw4XnCjJ4LbKOOoKKwAAAAAAAKA4SjkAvCId7W150zXrkiQDXR0Fp0mydH3Su1IpBwAAAAAAACiUUg4Ar1hXR1v6u9pz/Mx09r0wnmePnC4uTKlUucJqRCkHAAAAAAAAKI5SDgBVcXpqNv/l7j153e/ekTf8H1/LYyMniwsztC059FgyO11cBgAAAAAAAKClKeUAUDVTs3Pz87d9/BvFBRnclsxOJkeeKi4DAAAAAAAA0NKUcgComXK5XMzGg1sr46grrAAAAAAAAIBiKOUAUBWve9Xqs949d3S8gCRJVr0q6ehJRh8uZn8AAAAAAACg5SnlAFAVf/QLO856NzYxU0CSJO0dydprnZQDAAAAAAAAFEYpB4Cq6OlsP+vdmenZApJ839C2ZGR3UtQVWgAAAAAAAEBLU8oBoGpuvWzVguef/cO788DeY8WEGdyaTBxPTuwvZn8AAAAAAACgpSnlAFA1//EXbsoX3vva/PG7X7zK6tP37CsmzOC2yjj6cDH7AwAAAAAAAC1NKQeAqlnS05nrNyzL+uW98+862kvFhFl3XZJSMrq7mP0BAAAAAACAlqaUA0DVXbZ6YH7+/3xnb+bmyvUP0dWfrLrCSTkAAAAAAABAIZRyAKi6ro6FPy+/9pmHigkyuDUZcVIOAAAAAAAAUH9KOQDUxFd/7fb5+WcffL6YEEPbkhN7kzPHitkfAAAAAAAAaFlKOQDUxGVrBvKLr710/rlcLuAKq8GtlXH0kfrvDQAAAAAAALQ0pRwAaqavq31+vv/YmfoHGNxWGUddYQUAAAAAAADUl1IOADUzM/fi6ThnpmfrH2BgbTIwmIw+XP+9AQAAAAAAgJamlANAzawa6J6fv/ljd+bwqcn6hxjcqpQDAAAAAAAA1J1SDgA1849u3ZKff/Xm+efd+4/XP8TQtuTw48lMAYUgAAAAAAAAoGUp5QBQMx3tbfnJGzbMP3d1FPCzM7g1mZtJDj1W/70BAAAAAACAlqWUA0BN9XS++FPTVirVP8Dgtso4urv+ewMAAAAAAAAtSykHgJrq7mifn09Mz9Y/wIpLk66BZPTh+u8NAAAAAAAAtCylHABqqq/rpaWcufoHaGtL1l2vlAMAAAAAAADUlVIOADW1sr9rfj4+NVNMiKFtlVLOXAGlIAAAAAAAAKAlKeUAUFP93R35w394U5Lk2PhUMSEGtyZTY8mxZ4vZHwAAAAAAAGg5SjkA1Nybr12XjrZSjo9PFxNgcGtldIUVAAAAAAAAUCdKOQDUXKlUysxcOX/wtWdyxT//Yv7HowfrG2DNNUlbRzK6u777AgAAAAAAAC1LKQeAupqZK+c9//m++m7a2ZOsvspJOQAAAAAAAEDdKOUAUHfdHQX8/AxuTUaclAMAAAAAAADUh1IOAHW3vK+z/psObUvGRpOxQ/XfGwAAAAAAAGg5SjkA1MV/e8/N6etqT5IcPDmZ3fuP1zfA4NbK6AorAAAAAAAAoA6UcgCoi9suX51Hf+ut889/88hofQOsu74yjrrCCgAAAAAAAKg9pRwACjE+NVvfDftWJss2OykHAAAAAAAAqAulHADqaqC7I0lyeGyy/psPblXKAQAAAAAAAOpCKQeAuvrUL92SJHnm0Fj9Nx/alhx5Kpk6Xf+9AQAAAAAAgJailANAXV2/YVl+5qaNOXlmuv6bD25NUk4OPlr/vQEAAAAAAICWopQDQN0t7e3MyYmZ+m88uLUyjj5U/70BAAAAAACAlqKUA0DdLe3pzNjkTGZm5+q78bJNSc/yZPTh+u4LAAAAAAAAtBylHADqbllvR5LkRL2vsCqVKqflKOUAAAAAAAAANaaUA0DdrRzoTpIcG5+q/+aD25KD301mC7g+CwAAAAAAAGgZSjkA1N3Kvq4kydGxAko5Q9uSmYnk6NP13xsAAAAAAABoGUo5ANTd8r7OJAVcX5VUrq9KXGEFAAAAAAAA1JRSDgB1t7SnUso5OVHAFVKrr0zau5PRh+q/NwAAAAAAANAylHIAqLslPR1JklMTBZyU096ZrL3GSTkAAAAAAABATSnlAFB3PyjlnDxTwEk5SeUKq5HdSblczP4AAAAAAABA01PKAaDuOtrb0t/VnpNFnJSTJEPbkzMvJCf2F7M/AAAAAAAA0PSUcgAoxJKezmKur0qSoeHKOPJQMfsDAAAAAAAATU8pB4BCLO3tKPD6quuTUrtSDgAAAAAAAFAzSjkAFGJpT2dOTZ77pJwnD57K3Fy5dpt39iZrrkpGdtVuDwAAAAAAAKClKeUAUIglPec+KeeJ0VN588fuzO/f8XRtAwxtd1IOAAAAAAAAUDNKOQAUYklPZ05NnH1Szt4XxpMkX9g9UtsAQ8PJ2MHk1Ght9wEAAAAAAABaklIOAIXo7+7I2OTsWe8Pn5pMkjxx8FQmps/+vGqGtldGp+UAAAAAAAAANaCUA0AhBrrbc3ry7Our9hw9PT8/Pn72STpVM7g1SSk5sKt2ewAAAAAAAAAtSykHgEL0dXXkzPRsZufKSZKZ2bkkyR/e+b357xwbn6pdgO6BZPWrnJQDAAAAAAAA1IRSDgCFGOjuSJKMT83k5o98OVd86K/z9KGxBd/5t3/zeG1DDG1XygEAAAAAAABqQikHgEK0t5WSJF957FAOnpxMknzjqcMLvrO0p7O2IYa2Jyf3J6eP1HYfAAAAAAAAoOUo5QBQiNGTE0mSf/rpXfPvPnXPviTJPxhenyRnnZxTdUPbK+PIrh/+PQAAAAAAAIALpJQDQCF+6sYNZ7174uCpJMn/+oYrkiSPjpysbYjBbZXRFVYAAAAAAABAlSnlAFCIqweXZtPK3nN+dtnq/vn5+NRM7UL0Lk9WXJoccFIOAAAAAAAAUF1KOQAU5hsfeOM533e0v/jzdHRsqrYhhrY7KQcAAAAAAACoOqUcAAq1qr9rwfMX3vvaJMlHfnJrkuS7B07UNsD64eT4nuTMsdruAwAAAAAAALSUjqIDANDaPv2/3Jo7Hj+UDSt6s3ZJd67fsCxJ8rpXrU6SHB+frm2Aoe2VceSh5LLX13YvAAAAAAAAoGUo5QBQqCvWDuSKtQNnvV+zpDtJ8sG/fDgf/dITee8br8i7X3Np9QMMDVdGpRwAAAAAAACgilxfBcCi1NPZPj8/enoqv/n5R/M3j4xWf6O+lcmyzZVSDgAAAAAAAECVKOUA0DDufOpwbRYe2qaUAwAAAAAAAFSVUg4ADWNiarY2Cw8NJ0efTiZO1mZ9AAAAAAAAoOUo5QCwaH3yPbcseJ6YqVEpZ/1wZRx9uDbrAwAAAAAAAC1HKQeARevVl65c8Dw3V6ONhrZXRldYAQAAAAAAAFWilAPAotXeVlrw/DffHc2ffuvZ6m80sDZZMpSM7Kr+2gAAAAAAAEBLUsoBYFH7xgfekK/82u1Zt7Q7SfKbn380c3Pl6m80NOykHAAAAAAAAKBqlHIAWNQ2rezL5WsG8r/92JXz7+7fe6z6Gw1tT448mUydrv7aAAAAAAAAQMtRygGgIazs756fHzh+pvobDG1PynPJ6CPVXxsAAAAAAABoOUo5ADSEns4Xf7Ker0UpZ/1wZXSFFQAAAAAAAFAFSjkANIShZb3z8+eP1aCUs2Qo6V+jlAMAAAAAAABUhVIOAA3hirUD+dYH35hrhpZm9MRE9TcolSpXWI3sqv7aAAAAAAAAQMtRygGgYWxY3ps1S7pzZGyyNhsMDSeHHkuma1D6AQAAAAAAAFqKUg4ADWX7u50KAAAgAElEQVR1f1eOjE3VZvGh7Ul5Njn03dqsDwAAAAAAALQMpRwAGsrqJd05enoy5XK5+osPba+MIw9Vf20AAAAAAACgpSjlANBQVvV3ZWJ6LqenZqu/+PLNSe+K5MCu6q8NAAAAAAAAtBSlHAAayuqB7iTJ0bHJ6i9eKlVOy3FSDgAAAAAAAPAKKeUA0FBWDXQlSY7UopSTVEo5hx5NZqZqsz4AAAAAAADQEpRyAGgoPzgp58hYjUozQ9uT2ank8GO1WR8AAAAAAABoCUo5ADSUF0s5tTopZ7gyusIKAAAAAAAAeAWUcgBoKCv7K9dXHa3VSTkrLk26lyrlAAAAAAAAAK+IUg4ADaWroy3Lejtz6NREbTZoa0sGtyUHdtVmfQAAAAAAAKAlKOUA0HAuWd2fZw6drt0G64eTg48kszO12wMAAAAAAABoako5ADSca4eW5PHRk7XbYGh7MjORHHmydnsAAAAAAAAATU0pB4CGM7SsN8fGpzM1M1ejDbZXxhFXWAEAAAAAAAAXRykHgIazaqArSXJsfKpGG1yRdPYnIw/VZn0AAAAAAACg6SnlANBwhpb1JEmeOTRWmw3a2pPBrUo5AAAAAAAAwEVTygGg4axdUinl/MIf31O7TYa2JyO7k7kaXZEFAAAAAAAANDWlHAAaTrlcGWfmyrXbZP1wMn06Ofp07fYAAAAAAAAAmpZSDgAN5/K1/bXfZGh7ZXSFFQAAAAAAAHARlHIAaDh9XR3z88OnJmuzyeqrko6eZGRXbdYHAAAAAAAAmppSDgAN7T9989naLNzekay73kk5AAAAAAAAwEVRygGgIX3yPbckST7x9Wdy19NHarPJ0PZKKWdurjbrAwAAAAAAAE1LKQeAhnTD5uXz8/d9pkan2QxtTyZPJsefq836AAAAAAAAQNNSygGgIfV0ts/PD5yYqM0m64e/v8GDtVkfAAAAAAAAaFpKOQA0rOs3LK3tBmuvTTr7kn331nYfAAAAAAAAoOko5QDQsN7/lqvn5ycnpqu/QXtnsuGmZN+3q782AAAAAAAA0NSUcgBoWLdfuSYfevs1SZLnj52pzSabXp2M7E6mTtdmfQAAAAAAAKApKeUA0NCuHlqSJDk1MVObDTbdkpRnk+cfqM36AAAAAAAAQFNSygGgoS3p6UySjE3W4PqqJNm4ozLu+05t1gcAAAAAAACaklIOAA1toLsjSQ1Pyulbmay5WikHAAAAAAAAuCBKOQA0tOV9lZNyjo/X6KScJNn06mTfPcncXO32AAAAAAAAAJqKUg4ADW15bz1KObckE8eTI0/Wbg8AAAAAAACgqSjlANDQOtorP2Uf+/KLhZnp2bnMzpWrt8mmmyujK6wAAAAAAACA86SUA0DT+NlP3J0/u3dftv/r/563ffzO6i286vKkb5VSDgAAAAAAAHDeOooOAADVcs9zL+Se515Ikjx5cKx6C5dKldNylHIAAAAAAACA8+SkHAAa3j9+zSXnfD8zO1e9TTbdnBx9Ojl9pHprAgAAAAAAAE1LKQeAhrf36Pg534+enKjeJpturoz77qnemgAAAAAAAEDTUsoBoOF99Ge2n/P94VOT1dtk/Q1JW2ey79vVWxMAAAAAAABoWko5ADS8lf1dueN9r8+fvHtntm9cNv/+2PhU9Tbp7EnWDzspBwAAAAAAADgvSjkANIVLV/fnDVevzd/bvn7+3dGxKpZyksoVVs8/kMxU8QQeAAAAAAAAoCkp5QDQVP7uNevm5yfOTFd38U03J7OTycju6q4LAAAAAAAANB2lHACayqWr+/Psv3l72ttKOT5eg1JOkuz7dnXXBQAAAAAAAJqOUg4ATadUKmVpT0eOn6ny9VVL1iUrLkn2fae66wIAAAAAAABNRykHgKa0vK8rJ87MVH/hTbcke7+TlMvVXxsAAAAAAABoGko5ADSlZb2dOT5e5ZNykmTTq5PTh5Jjz1Z/bQAAAAAAAKBpdBQdAABqYde+40mSE2ems6y3s3oLb76lMu67J1l5WfXWBQAAAAAAAJqKk3IAaGqPjZys7oJrrk66lyZ7v13ddQEAAAAAAICmopQDQFO6432vT5I8fWisugu3tScbd1ZOygEAAAAAAAB4GUo5ADSljSt6kyQvnJ6q/uKbbk4OPZqcOV79tQEAAAAAAICmoJQDQFPqbG9Lf1d7jo9PV3/xzTcnKSfP31f9tQEAAAAAAICmoJQDQNNa3teVE2dqUMrZcFNSakv2fqf6awMAAAAAAABNQSkHgKa17P9n706D7LwO80C/X+8AGnsDBBoNEiQBSJQoggABkhIpyY6W0RJ5kSzHi+yMLZfHiRMltmbi8ZTjTJKSRrY8jqxxpCRlW0okOxXHskaxPI522hIpkQTBTZRILCRAdGPtxr50A43+5scFm0awdQP3dve993mqUOf7zj333Pdfs4pvnTOrPUdP1+D6qs65yQ23J7uVcgAAAAAAAIBLU8oBoGEtmN1em+urkmTlPUn/5uTcaG32BwAAAAAAAOqaUg4ADWvB7PbaXF+VJDfem5w9mRx4pjb7AwAAAAAAAHVNKQeAhrVgdkd2HTqVX/3TJ3LoZJWvsVp5d2V80RVWAAAAAAAAwMWUcgBoWD3dnTkzOpY/3zKQj/zV96u7+fyVydzeZLdSDgAAAAAAAHAxpRwAGtaS7o7x56qflFMUldNylHIAAAAAAACAS1DKAaBhzZvVPv781e8fyJ8+uru6P3DjvcnR3cnRgeruCwAAAAAAANQ9pRwAGlZLUVzw/s8+91SGz56r3g+svLsyOi0HAAAAAAAA+B8o5QDQsDatWnTR3C999rHq/cCyO5K2WcnuR6q3JwAAAAAAANAQlHIAaFjL5ndl50femR98xZLxuQeeO1i9H2htT1bclez+TvX2BAAAAAAAABqCUg4ADe+T77urdpvfeE+y96nkzMna/QYAAAAAAABQd5RyAGh4Xe2t+YlNK2uz+cp7kvJcMrClNvsDAAAAAAAAdUkpB4Cm0N768p+8vUdPV2/jvk2VcffD1dsTAAAAAAAAqHtKOQA0hQ++de3489P9R6u38exFSc8rlHIAAAAAAACACyjlANAUFszuyHd+/U1Jkl/8zGP57kAVizk33pPsfiQZG6vengAAAAAAAEBdU8oBoGksmtMx/vw32w5Wb+OV9yTDR5LBrdXbEwAAAAAAAKhrSjkANI2Otpf/7D2283D1Nl55b2V0hRUAAAAAAABwnlIOAE3lN955W5Lka88eqN6mi29NZi1SygEAAAAAAADGTaiUUxTFrUVRdJ5//oGiKD5QFMWC2kYDgOr7hdffUv1NiyK58d5k10PV3xsAAAAAAACoSxM9KedzSc4VRbE6yR8muTnJn9QsFQDU0P/8ulVJkuGz56q36ar7k8MvJEcHqrcnAAAAAAAAULcmWsoZK8tyNMmPJvlYWZa/kmR57WIBQO3curQ7SXJs+Gz1Nr3pvsq468Hq7QkAAAAAAADUrYmWcs4WRfGTSf5+ki+en2uvTSQAqK15XW1Jkqf7j1Zv02WvSTrnJzu/Vb09AQAAAAAAgLo10VLOzyV5bZIPlWX5QlEUNyf5bO1iAUDt9C2clSR5/3/cnNFzY9XZtKU1uel1SjkAAAAAAABAkgmWcsqy/F5Zlh8oy/I/F0WxMMncsiw/UuNsAFATd65cOP78+9/YXr2NV92XHNqRHNtbvT0BAAAAAACAujShUk5RFA8URTGvKIpFSZ5M8qmiKH63ttEAoDZaW4rx5499dVu+v/dYdTZedX9l3PVgdfYDAAAAAAAA6tZEr6+aX5blsSTvTvKpsizvSvLm2sUCgKnz9t/7ZvYcOX39Gy27I+mcl+z85vXvBQAAAAAAANS1iZZy2oqiWJ7kx5N8sYZ5AGBKPPmbb73g/dl9VTgtp6U1ufG1yU4n5QAAAAAAAECzm2gp518l+VKSHWVZPloUxS1JttUuFgDU1vzZ7Re8/8bnv1udjVfdnwxtS47vq85+AAAAAAAAQF2aUCmnLMv/WpblHWVZ/oPz78+XZfme2kYDgNp694YV4897jg5XZ9NV91XGXU7LAQAAAAAAgGY2oVJOURR9RVF8viiKA0VR7C+K4nNFUfTVOhwA1NKP3LnigvexsTLDZ89l8MTItW+6bF3SMTfZ+a3rTAcAAAAAAADUs7YJrvtUkj9J8t7z7+87P/eWWoQCgKmwrm9Beud3jZ+Sc/eHvzZeyPn6B9+YW5Z0T37T1rbkxnuVcgAAAAAAAKDJTeiknCRLyrL8VFmWo+f/fTrJkhrmAoCamz+7PQ/9+pvyc/etSpILTsjZduDEtW+86v5kcGty4sB1JgQAAAAAAADq1URLOYNFUbyvKIrW8//el2SolsEAYKq8Z8PFNzKOjZXXvuGq+yuj03IAAAAAAACgaU20lPPzSX48yb4ke5P8WJKfq1UoAJhKc7suvs1x/7Hha99w+bqkozvZ9eB1pAIAAAAAAADq2YRKOWVZvliW5Q+VZbmkLMulZVn+SJJ31zgbAEyJ+bPaL5r70839175ha3ty471OygEAAAAAAIAmNtGTci7lV6uWAgCm0d8u5Tz2G29Oknxv77Fs3nno2je96b7k4LPJiYPXGw8AAAAAAACoQ9dTyimqlgIAplFRvPwnbdGcjvHnfddzhdWq11dGV1gBAAAAAABAU7qeUk5ZtRQAMM12fuSd2fmRd6Yoimy8aWGS5Nf//OmcGR27tg1770za5yjlAAAAAAAAQJO6YimnKIrjRVEcu8S/40l6pygjAEypP/q5TUmS48OjeWzX4WvbpLU9ufGeZOe3qpgMAAAAAAAAqBdXLOWUZTm3LMt5l/g3tyzLtqkKCQBTaV5X+/jzl57Zd+0brbo/OfC95ORQFVIBAAAAAAAA9eR6rq8CgIbV3lokST790M5r3+Sm+yujK6wAAAAAAACg6SjlAMAlbPvQO1IUSU93x7Vv0rs+aZ/tCisAAAAAAABoQko5AHAZP37XyrS2FNe+QVtHsvJuJ+UAAAAAAABAE1LKAYDL6O5qy4nh0evbZNX9yf7vJqcOVScUAAAAAAAAUBeUcgDgMuZ2teXkmXM5Mzp27Zusen1l3PVQdUIBAAAAAAAAdUEpBwAuo3fBrCTJ/mPD17HJhqRtVrLzW1VKBQAAAAAAANQDpRwAuIx5XW1JkhMj13GFVVtHsvJupRwAAAAAAABoMko5AHAZszsqpZyT11PKSZJV9yf7v5ucOlSFVAAAAAAAAEA9UMoBgMuY01kp5Tz8wnWWaVbdn6RMXvz29YcCAAAAAAAA6oJSDgBcxuyO1iTJR7/0XFb973+Z3YdOpSzLyW+04q6krSvZ+WCVEwIAAAAAAAAzlVIOAFzGzT1zLnh//W9/I//8C9+d/EZtnUnfpmTnN6uUDAAAAAAAAJjplHIA4DK62luzZmn3BXOf/c6L17bZqtcn+55OTh+pQjIAAAAAAABgplPKAYAr+Jc//OrqbLTqviRl8uK3q7MfAAAAAAAAMKMp5QDAFbzu1p587YNvvGBu+Oy5yW+0YmPS2pns/FaVkgEAAAAAAAAzmVIOAFzFrUu687pbF4+/7zs6PPlN2ruSvk1KOQAAAAAAANAklHIAYAI+8/578smf3pAkOT48em2brLo/2fdUMny0iskAAAAAAACAmUgpBwAmoLWlyILZHUmS48Nnr22TVfcn5Vjy4neqmAwAAAAAAACYiZRyAGCC5na1JUmOj1zjSTl9G5PWjmTnN6uYCgAAAAAAAJiJlHIAYILGSznXen1V+6ykb1Oy88EqpgIAAAAAAABmIqUcAJiguV3tSa7j+qokuem+ZO8TyfCxKqUCAAAAAAAAZiKlHACYoO7Oykk5J671pJwkuem1STmW7NlSpVQAAAAAAADATKSUAwAT1NHWks62lhwfuY5STu/6yjiglAMAAAAAAACNTCkHACZh/qz2HDl15to3mLUwWXhzsufx6oUCAAAAAAAAZhylHACYhCVzOzN44jpKOUmyYkOy54nqBAIAAAAAAABmJKUcAJiEJXM7c/D4yPVt0rs+OfpicnKwOqEAAAAAAACAGUcpBwAmoaO1JU8PHM3YWHntm/Sur4yusAIAAAAAAICGpZQDAJPw5e/tT5J8a/t1nHKzfF2SIhnYUp1QAAAAAAAAwIxTs1JOURRdRVE8UhTFk0VRPFMUxb+s1W8BwFT5p29ekyT52T96JF9/dv+1bdI5N+lZ66QcAAAAAAAAaGC1PClnJMnfKctyXZI7k7ytKIp7a/h7AFBzP3znivHnv3xq37VvtGJDsmdLUl7HNVgAAAAAAADAjFWzUk5ZceL8a/v5f/7PIwB17eaeOePPn9vSf+0b9a5PTuxPju+tQioAAAAAAABgpqnlSTkpiqK1KIonkhxI8pWyLB++xJpfLIpic1EUmw8ePFjLOABQFd/6tR8cf945ePLaNundUBkHtlQhEQAAAAAAADDT1LSUU5blubIs70zSl+Tuoihuv8Sa/1CW5cayLDcuWbKklnEAoCr6Fs4ef953bPjaNll2e1K0Jnser1IqAAAAAAAAYCapaSnnJWVZHknyQJK3TcXvAUCtFUVl/In/8J1r26B9VrL0VUo5AAAAAAAA0KBqVsopimJJURQLzj/PSvLmJM/W6vcAYCr9xT+6//o3WbE+2bMlKcvr3wsAAAAAAACYUWp5Us7yJN8oiuKpJI8m+UpZll+s4e8BwJRZc0P3+POLQ6eubZPe9cnpw8mRXVVKBQAAAAAAAMwUNSvllGX5VFmW68uyvKMsy9vLsvxXtfotAJhqnW2t+RfvelWS5KEdg9k1dDKDJ0Ymt0nvhso4sKXK6QAAAAAAAIDpVsuTcgCgob35thuSJGfOjeWNH30g9//W1ye3wdJXJa0dyZ7Ha5AOAAAAAAAAmE5KOQBwjTrbKn9Gf/MLzyRJhs+OZfjsuXzhiYH8zB8+nIefH7ryBm0dybLXKOUAAAAAAABAA2qb7gAAUK8621ovmnv7730zLwyeTJI8tGMoOz78jitv0rs+efK/JGNjSYuuLAAAAAAAADQK//cPAK5RZ/vFf0ZfKuQkybmx8uqb9G5IzhxPhrZXMxoAAAAAAAAwzZRyAOAadbVffFLO/+j7e49deUHv+sroCisAAAAAAABoKEo5AHAdfvUta/N371ie//Wta8fnWluK8edf/uMtV96gZ23SPjvZc5V1AAAAAAAAQF1pm+4AAFDPPvCmNePPv/PlrUmSHR9+R57YfSQ/8m8fTN+i2VfeoLUtWb7OSTkAAAAAAADQYJRyAKBK3nzb0oyMjiVJ7ly5IK+7dXGOnDpz9S/2rk82fyo5N1op6QAAAAAAAAB1z/VVAFAlf/D3N+Uz779n/P3VvfPy7L7jOTdWXvmLvRuS0dPJwWdrnBAAAAAAAACYKko5AFAjtyzpzpnRsew5cvrKC3vXV0ZXWAEAAAAAAEDDUMoBgBq5YV5nkmTwxMiVFy66Jemcn+zZMgWpAAAAAAAAgKmglAMANdLT/VIp58yVF7a0JL3rnJQDAAAAAAAADUQpBwBqZPH5Us7Q1U7KSZLeDcm+7yajE1gLAAAAAAAAzHhKOQBQIz3dHSmKZO/R4asv7l2fjJ1N9j9T+2AAAAAAAABAzSnlAECNdLa1Ztm8rvQfPn31xb3rK+OeLbUNBQAAAAAAAEwJpRwAqLHvPD909UULbkxmL072PF77QAAAAAAAAEDNtU13AABoZC9dXXVurExrS3H5hUVROS1nQCkHAAAAAAAAGoGTcgCghm5ZMidJ8uKhU1df3LshOfj95MwE1gIAAAAAAAAzmlIOANTQx/7enUmSZ/ceu/ri3vVJOZbse7rGqQAAAAAAAIBaU8oBgBpas3RuiiJ5bv/xqy/uXV8Z92ypbSgAAAAAAACg5pRyAKCGZnW05oa5Xdk6kVLOvOXJ3OXJnsdrHwwAAAAAAACoKaUcAKixZfO78pXv7U9Zlldf3Ls+GXBSDgAAAAAAANQ7pRwAqLG33b4sZ8+VGTxx5uqLezckQ9uS4WO1DwYAAAAAAADUjFIOANTYmqXdSZL+w6euvrh3fWXc+0QNEwEAAAAAAAC1ppQDADXWt3B2kqT/8OmrL36plLPn8RomAgAAAAAAAGpNKQcAamzFwllJJljKmbM4WXBjMrClxqkAAAAAAACAWlLKAYAa6+5sS5I88NyBiX2hd4OTcgAAAAAAAKDOKeUAwBR5+IVD+T//2zP53a9szcHjI5df2Ls+ObIrOXVo6sIBAAAAAAAAVaWUAwBT4D0b+pIkn35oZz7+tW3Z9KGvZvuBE5devGJDZdzjCisAAAAAAACoV0o5ADAF3rC256K5LbsOX3rx8nWV0RVWAAAAAAAAULeUcgBgCvzQut589MfuuGCuKC6zuGt+snh1MqCUAwAAAAAAAPVKKQcApkBRFHn3+SusXnLwxEiOD5+99Bd6NzgpBwAAAAAAAOqYUg4ATJHWliKP/B9vyhf/8f1Jkt/+78/lvf/u25de3Ls+Ob4nOb5vChMCAAAAAAAA1aKUAwBTaOm8rvQtnDX+/uy+4/ndLz+XU2dGL1y4YkNldFoOAAAAAAAA1CWlHACYYnM62y54//jXt+ejX3ruwkXLXpMULcnAlilMBgAAAAAAAFSLUg4ATLH21ov//H7qwZ0XTnTMSZa+Khl4bGpCAQAAAAAAAFWllAMAM8Dy+V0XT67YUCnllOXUBwIAAAAAAACui1IOAMwAqxbPuXhyxcZk+Ehy6PmpDwQAAAAAAABcF6UcAJgGn/zpDRe8t1zqL3LfxsrYv7n2gQAAAAAAAICqUsoBgGnw9tcsz32rF4+/Hx8eHX/e8uLhPP7i4WTJK5P2OcmAUg4AAAAAAADUm7bpDgAAzeqT77srX3xybx7deSiPvHBofP7dn3goSbLzI+9Metc7KQcAAAAAAADqkJNyAGCazOtqz0/dc2OWzu3MweMjKcvygs/3HDmd9N2V7Hs6GR2ZppQAAAAAAADAtVDKAYBptnReV86cG8vR02cvmP/+3mPJio3J2NlKMQcAAAAAAACoG0o5ADDNbpjXmSQZOHL6gtNyjg2fTVbcVXlxhRUAAAAAAADUFaUcAJhmd65ckCT59o6hjIyOjc+fGDmXzF+RzF2eDDw2XfEAAAAAAACAa6CUAwDTrG/h7CTJX289mH1Hh8fnT42MVh5W3JUMOCkHAAAAAAAA6olSDgDMEN/cNpgf/cSD4+8nXyrl9G1MDj2fnDo0TckAAAAAAACAyVLKAYAZ5PCps+PPJ0bOVR5W3FUZXWEFAAAAAAAAdUMpBwBmqD968IXKQ+/6JIVSDgAAAAAAANQRpRwAmAG+/CtvuOT8ubEy6ZybLL0t6d88xakAAAAAAACAa6WUAwAzwNob5mb7h96eJHnzbTeMz//11gOVhxUbKifllOV0xAMAAAAAAAAmSSkHAGaIttaWPPuv35Z//zN35e23L0uSfOrBnZUPV2xMTh9KDr8wfQEBAAAAAACACVPKAYAZpKu9Na0tRT7x0xuSJJ1t5/9U922sjP2PTVMyAAAAAAAAYDKUcgBgBiqKIm991Q35/t7jKcsyWXJb0j47Gdg83dEAAAAAAACACVDKAYAZ6o6++Rk4cjqHTp5JWtuS5Xcm/Uo5AAAAAAAAUA+UcgBghrp1SXeSZP+xkcpE313JvqeS0TPTmAoAAAAAAACYCKUcAJihls7rSpLsPzZcmVixMTl3Jtn/9DSmAgAAAAAAACZCKQcAZqjeBZVSztefPVCZ6NtYGfsfm6ZEAAAAAAAAwEQp5QDADHXD3Eop59DJ89dVzVuRdN+QDCjlAAAAAAAAwEynlAMAM1RLS5HXr+lJ/+FTlYmiqFxhNbB5eoMBAAAAAAAAV6WUAwAzWN/C2ek/fPpvTdyVDG1PTh+evlAAAAAAAADAVSnlAMAMtqS7I0Mnz2RsrMyuoZP5ha+WlQ9cYQUAAAAAAAAzmlIOAMxgszrakiSbdx3OGz/6QL4zclPKFMnAlmlOBgAAAAAAAFyJUg4AzGBvWNuTJHnguQNJkhOZnf2dNyX9m6czFgAAAAAAAHAVSjkAMIMt6e5MknzigR3jc9vbX5kMbE7KcrpiAQAAAAAAAFehlAMAM9iiOR0pigvn/upwb3JqKDmya3pCAQAAAAAAAFellAMAM1hba0te3Ttv/P21tyzOE2OrKy+usAIAAAAAAIAZSykHAGa47w4cG3/+jz9/d54tV+Z02ZHP/cUX8tiuw9OYDAAAAAAAALgcpRwAmOH+n59cnyT541+4Jx1tLTmX1jxd3pxVw9/Pr33uqWlOBwAAAAAAAFxK23QHAACu7F3revOudb3j75tWLcyT/bfmZ1u/kkPHTkxjMgAAAAAAAOBynJQDAHVmdkdbnhhbnc7ibHpHnk9Zlnlo+2CGz56b7mgAAAAAAADAeUo5AFBn3ruxL0+M3ZokubNlR/6/p/flp/7g4fyLLzwzzckAAAAAAACAlyjlAECdWTavKwPpycFyXta3bM8v/8mWJMlDzw9OczIAAAAAAADgJUo5AFBn1q1ckA+8aW3K3ruyrtgxPj9ydmwaUwEAAAAAAAB/m1IOANSZ9taW/Opb1mbpbfdldcuezMvJJMnw2XPTnAwAAAAAAAB4iVIOANSrFRuTJHe0PJ8kOTY8mrIspzMRAAAAAAAAcJ5SDgDUq971SZJ1xY684zXLkiR7jg5PZyIAAAAAAADgPKUcAKhXsxZk+1hv7mzZnh/fuDJJsu/o6WkOBQAAAAAAACRKOQBQ1+atvjevn70r87vakiRHT5+d5kQAAAAAAABAopQDAHVt6Stfl66RoSwa3Z8kOT48Os2JAAAAAAAAgEQpBwDqW9/GJMmCw08lSf7bE3vy/z4+kJHRc9OZCgAAAAAAAJqeUg4A1LMbbk/autJ98MkkydeePfe0h7EAACAASURBVJB/+l+eyJ891j/NwQAAAAAAAKC5KeUAQD1rbU+Wr0vrnscumH5o+9A0BQIAAAAAAAASpRwAqH8r7kr2PpH1K+aMT/3l03unMRAAAAAAAACglAMA9a5vUzI6nM+/Z8EF0yOj56YpEAAAAAAAAKCUAwD1rm9TZex/NO9a1zs+fWJ4dJoCAQAAAAAAAEo5AFDv5vcl3cuS/kfzsb93Z37tba9Mkjz8wqFpDgYAAAAAAADNSykHAOpdUSR9G5P+R9PaUuTA8eEkyW9+4ZlpDgYAAAAAAADNSykHABpB36bk0PPJyaF84O+sSZL8wCuWTHMoAAAAAAAAaF5KOQDQCPo2VcaBzVk4pyNrlnbn+PDZ6c0EAAAAAAAATUwpBwAaQe/6pGhNdj+SJJnd0ZovPbM/v//1bdMcDAAAAAAAAJqTUg4ANIKO2cmy25P+R5MkT/YfTZL8zpe35vDJM9OZDAAAAAAAAJqSUg4ANIq+TcnAlmTsXN6zoW98+t//zfPTGAoAAAAAAACak1IOADSKvk3JmePJwefyoR+9PUVRmT5yykk5AAAAAAAAMNWUcgCgUfRtqoz9j6arvTV/8Y/uT5K8qnfeNIYCAAAAAACA5qSUAwCNYtEtyaxFSf8jSZKbe+YkSU6fOTedqQAAAAAAAKApKeUAQKMoisppOf2bkySzO1pTFMmJkdEkyZO7j+TB7YPTmRAAAAAAAACahlIOADSSvk3JwWeT00dSFEW6O9tyfLhSyvnhf/tgfvoPHp7mgAAAAAAAANAclHIAoJH0bayMe7YkSeZ2tuXEyGgOHBseXzJ4YmQ6kgEAAAAAAEBTUcoBgEay4q4kxfgVVnuODufPHuvP3R/+2viS93/60WkKBwAAAAAAAM1DKQcAGknXvGTpbUn/5Ys33993fAoDAQAAAAAAQHNSygGARtO3sVLKKct0tV/8p/7M6Ng0hAIAAAAAAIDmopQDAI2mb1Ny+nAytCNjl+nfPNV/ZGozAQAAAAAAQJNRygGARtO3qTL2P5o//4evG59euWjW+PMP/f6DU50KAAAAAAAAmopSDgA0mp5XJJ3zkv5H8+reeePTH/2xdblv9eJpDAYAAAAAAADNQykHABpNS0uyYkPS/2iKohifvveWxTl6+uz4+54jp6cjHQAAAAAAADQFpRwAaER9dyf7n0nOnMw/edOafPAta5Mkn33/PeNL3vcHD09XOgAAAAAAAGh4SjkA0Ij6NiXluWTP4/mVt6zNP37TmiTJgtkd+at/8vokySuXz53OhAAAAAAAANDQlHIAoBH1bayM/Y9e9NFty+elb+GsdLa15ondRzJ6bmyKwwEAAAAAAEDja5vuAABADcxelCy6NenffMmPx8bKfP7xgXz+8YEkyc/ce1P+9Y/cPpUJAQAAAAAAoKE5KQcAGlXfpspJOWV50Ud7jw1f8P6Z7+zK2NjF6wAAAAAAAIBro5QDAI1q5abkxP7k6O6LPvpPP3/3RXNfeHJgKlIBAAAAAABAU1DKAYBG1bepMu5+5KKPXnvL4ovm/tmfPVXrRAAAAAAAANA0lHIAoFEtfXXSNivp33zRR22tLenp7rhg7qfuvnGqkgEAAAAAAEDDa5vuAABAjbS2JSs2JP2PXvLjr33wB/LMnqPZvPNw/uyx/hw9fXaKAwIAAAAAAEDjclIOADSyvo3JvqeS0ZGLPpo/qz2vu7UnH3jTmiyc3Z7Dp5RyAAAAAAAAoFqUcgCgkfXdnZw7k+x96orLFs7pyOFTZ6YoFAAAAAAAADQ+pRwAaGR9Gytj/yNXXPbk7iN5qv9ojjotBwAAAAAAAKpCKQcAGtncZcn8G5P+R6+4bMncziTJjsETU5EKAAAAAAAAGp5SDgA0ur6NSf/mKy758I++Jkly9LSTcgAAAAAAAKAalHIAoNH1bUqO7k6O7b3skpdOyhk6cWaqUgEAAAAAAEBDU8oBgEbXt6kyDlz+tJye7kopZ/DEyFQkAgAAAAAAgIanlAMAjW75HUlrR9L/6GWXzO5oTVd7SwaPK+UAAAAAAABANSjlAECja+tMlq9Ldl++lFMURXq6O52UAwAAAAAAAFWilAMAzaBvU7Ln8eTc2csu6enuzNDJM1MYCgAAAAAAABqXUg4ANIO+jcno6WT/M5ddsnRuZ/YdHZ7CUAAAAAAAANC4lHIAoBn0baqM/Ze/wuqWJd3ZOXQyo+fGpigUAAAAAAAANC6lHABoBvNXJt3Lkv7Nl12yZml3zp4r8/zgySkMBgAAAAAAAI1JKQcAmkFRVK6w6n/ksktevWJekmTLrsNTlQoAAAAAAAAallIOADSLvk3JoeeTk0OX/Hj1ku4URfLYrsOusAIAAAAAAIDrpJQDAM2ib1NlHLj0FVZtrS0py+S/Ptafj39t2xQGAwAAAAAAgMajlAMAzWL5HZVx/zNXXfrX2wZrHAYAAAAAAAAam1IOADSLzrnJ3OXJ4NVPwWktpiAPAAAAAAAANDClHABoJj1rkqGrl3K2vHgkA0dOT0EgAAAAAAAAaExKOQDQTBavSQa3JmV5yY//7/euG39+/6cfnapUAAAAAAAA0HCUcgCgmfSsTYaPJicHL/nxe+7qG39+dt/xqUoFAAAAAAAADUcpBwCaSc/qyji49bJLdnz4HePP5WVO1AEAAAAAAACuTCkHAJrJ4jWVcWjbZZe0thSZ1d6aJDl99txUpAIAAAAAAICGo5QDAM1k/sqkrSsZvHwpJ0n++d99VZLk2OnRqUgFAAAAAAAADUcpBwCaSUtLsnj1VUs582a1JUne8NFv5I0f/UZGRp2YAwAAAAAAAJOhlAMAzaZnzRWvr0qSeV3tSZIzo2PZNXQqn3xgx1QkAwAAAAAAgIahlAMAzWbxmuTwzmR05LJLblky54L3j331yiUeAAAAAAAA4EJKOQDQbHrWJuVYcuiFyy5ZsWDWBe+vX9NT61QAAAAAAADQUJRyAKDZ9KyujINbL7ukKIo8/+F35CfvXpme7o4cHx6donAAAAAAAADQGJRyAKDZLF5TGYeufCVVS0uR/+vdd+SNa5dm79HTUxAMAAAAAAAAGodSDgA0m87uZG5vMnjlUs5LXrlsbvYfG8mhk2dqHAwAAAAAAAAah1IOADSjnjUTLuXcuHh2kmTgsNNyAAAAAAAAYKKUcgCgGb1UyinLqy5dPKcjSXL4lJNyAAAAAAAAYKKUcgCgGfWsTUaOJicPXnXp/FntSZIjp8/WOhUAAAAAAAA0DKUcAGhGi1dXxsGtV106f3allPOH33qhlokAAAAAAACgoSjlAEAz6llbGQe3XXXp4jmdSZIndx/JN7dd/WQdAAAAAAAAQCkHAJrTvBVJ26wJlXJaW4rx568/e6CWqQAAAAAAAKBhKOUAQDNqaalcYTV09VJOkszuaE2SHD11tpapAAAAAAAAoGEo5QBAs+pZkwxundDSWe2VUs6fPz5Qy0QAAAAAAADQMJRyAKBZ9axJjryYnB2+6tLP/sI948/b9h+vZSoAAAAAAABoCEo5ANCsetYm5Vhy6PmrLr1t+bz8L2+4JUnyln/zN7VOBgAAAAAAAHVPKQcAmtXi1ZVxaNuElv/SG29NkqxYMKtWiQAAAAAAAKBhKOUAQLN6qZQzuHVCyxfO6UiSDBw5XatEAAAAAAAA0DCUcgCgWXV2J/NWJIPbJ/3V7+05VoNAAAAAAAAA0DiUcgCgmfWsmfD1VUmyadXCJMnvfPm5WiUCAAAAAACAhqCUAwDNbPGaZHBbUpYTWv5b77kjSXLT4tm1TAUAAAAAAAB1TykHAJpZz9pk5Fhy4sCElt/cMydJ8qkHd2b47LlaJgMAAAAAAIC6ppQDAM2sZ3VlHNw6oeVFUYw/Hzg2UotEAAAAAAAA0BCUcgCgmfWsrYxD2yb8lV9/+yuTJMeGz9YiEQAAAAAAADQEpRwAaGZze5P22cngxEs5r1kxP0lyYmS0VqkAAAAAAACg7inlAEAza2lJFq+eVCmnu6stSXJiWCkHAAAAAAAALkcpBwCaXc+aZHDrhJfP7WpPkmw7cKJWiQAAAAAAAKDuKeUAQLNbvCY58mJydnhCy7s7Kyfl/NZ/f7aWqQAAAAAAAKCuKeUAQLPrWZOkTA7tmNDyRXM6apsHAAAAAAAAGoBSDgA0u541lXFw24SWt7YU48+j58ZqkQgAAAAAAADqnlIOADS7xasr4wRLOUnyjtcsS5IcOnWmFokAAAAAAACg7inlAECz65iTzOtLhiZeynnXHb1JkqETSjkAAAAAAABwKUo5AEDlCqvBrRNevri7M4lSDgAAAAAAAFyOUg4AcL6Usz0pywktXzSnI0kydHKklqkAAAAAAACgbinlAABJz9rkzPHk+L6JLe+ulHIGnZQDAAAAAAAAl6SUAwAki1dXxqFtE1o+r6s9bS1Fhk44KQcAAAAAAAAuRSkHAKiclJMkg1sntLylpUhLUeQTD+zI1v3HaxgMAAAAAAAA6pNSDgCQzOtN2uckg9sn/JUz58aSJG/9N39Tq1QAAAAAAABQt5RyAICkKJKe1RM+KSdJls7trGEgAAAAAAAAqG9KOQBAxeI1ydC2CS//5R9cPf78u19+rhaJAAAAAAAAoG4p5QAAFT1rkyO7k7OnJ7T8Z1970/jzx78+8WuvAAAAAAAAoBko5QAAFT2rk5TJ0I4JLS+K4oL3sbGyBqEAAAAAAACgPinlAAAVi9dUxklcYfV7P3Hn+POJM6PVTgQAAAAAAAB1SykHAKhYvLoyDk68lPPDd67I//Y/vSJJcuDYcC1SAQAAAAAAQF1SygEAKjpmJ/NXTqqUkyTHhysn5PzDP95Si1QAAAAAAABQl5RyAICX9axJBrdO6is/dldfkuSGeV21SAQAAAAAAAB1SSkHAHjZ4jXJ0PakLCf8ldVLu3Nzz5wcPnWmhsEAAAAAAACgvijlAAAv61mTnDmRHN87qa8tn9+VA8dGahQKAAAAAAAA6o9SDgDwsp41lXFw26S+9oa1S3Lg+Ei27j9eg1AAAAAAAABQf5RyAICX9aytjINbJ/W1u25amCT54lOTO2EHAAAAAAAAGpVSDgDwsrnLk47uZGj7pL726t55SZKPf21yJ+wAAAAAAABAo1LKAQBeVhTJ4tWTPilndkdbjQIBAAAAAABAfVLKAQAu1LMmGZzcSTlJ8poV85Mk39x2sNqJAAAAAAAAoO4o5QAAF+pZmxx9MTlzalJf+6U33pok2Xd0uBapAAAAAAAAoK4o5QAAF1q8ujIe2jGpr73xFUuSJEMnz1Q7EQAAAAAAANQdpRwA4EI9ayvj4NZJfW1OR2uS5CN/9Wy1EwEAAAAAAEDdUcoBAC60+NYkRTK4fVJfK4qiNnkAAAAAAACgDinlAAAXap+VzF856ZNykmTRnI4kycCR09VOBQAAAAAAAHVFKQcAuFjP6mRo26S/1tVW+U+LXYMnq50IAAAAAAAA6opSDgBwsUW3JId2JmU5qa/9znvXJUmOnj5bg1AAAAAAAABQP5RyAICLLbw5GTmanD48qa/1LpiVJPkHf7wlb/jtb+TkyGgt0gEAAAAAAMCMp5QDAFxs0c2V8dALk/raqp45WXG+mPPioVP59o6haicDAAAAAACAuqCUAwBcbOH5Us7hyZVykuSVy+aOPw+PnqtWIgAAAAAAAKgrSjkAwMUWrqqMkzwpJ0n2HB0ef/7uwDFXWAEAAAAAANCUlHIAgIt1zE66l13TSTkdrcX487/76x35xc9srmYyAPj/2bvv+Krre4/jr5OdQAIJYa+EEVkiQlSEouICRb3uah292tpabdXejqtWba0dtq5rW621S6uotVVrFUTcGxUURVbYS1ZIgOx57h8Ho2kYITkjJK/n43Eev9/5zfeh/YNHeffzlSRJkiRJkqQDgqUcSZK0e1m5LZqUc9OpIxnZJ6Ph+1vLt4UzlSRJkiRJkiRJknRAsJQjSZJ2LzO3RZNyxg3MZMZVkxq+pycnhDOVJEmSJEmSJEmSdECwlCNJknYvKxdKNkJNRYtu/3J+fwB6dUkJZypJkiRJkiRJkiTpgOD/dV2SJO1eZm5oW7waegzf79t/dfZo6oJB3l5eGN5ckiRJkiRJkiRJ0gHASTmSJGn3sgaFtkX7v4TVZzbvrOTTHZVUVNeFKZQkSZIkSZIkSZJ0YLCUI0mSdi/rs0k5LS/ljOnfFYA5q7aFI5EkSZIkSZIkSZJ0wLCUI0mSdi81E5K7tGpSzoXjBwKwobgiXKkkSZIkSZIkSZKkA4KlHEmStHuBAGTltGpSTrdOScQFQstYSZIkSZIkSZIkSR2JpRxJkrRnmbmtmpSTEB9H9/RkNu2wlCNJkiRJkiRJkqSOxVKOJEnas6xc2L4W6uta/IheGSlsLqkKYyhJkiRJkiRJkiSp7bOUI0mS9iwzF+prYMf6Fj+iR0YKm52UI0mSJEmSJEmSpA7GUo4kSdqzrNzQtrjlS1j1ykhhw/YKgsFgmEJJkiRJkiRJkiRJbZ+lHEmStGeZu0o5RS0v5fTMSKa0qpZvPfxBmEJJkiRJkiRJkiRJbZ+lHEmStGcZfSA+qVWTcmrqQhNyZi3cFK5UkiRJkiRJkiRJUptnKUeSJO1ZXDx0HdiqSTljB2aGMZAkSZIkSZIkSZJ0YLCUI0mS9i4rt1WlnKPzunPaIX0AKNhcEq5UkiRJkiRJkiRJUptmKUeSJO1dZm5o+apgsMWPWL2tDIAT73qdsqracCWTJEmSJEmSJEmS2ixLOZIkae+ycqG6FMoKW/yIO88d07C/vrgiHKkkSZIkSZIkSZKkNs1SjiRJ2rvM3NC2uOVLWA3p0ZmxA7oC8NH67VTX1ocjmSRJkiRJkiRJktRmWcqRJEl7l7WrlFPU8lIOwP0X5wPww39+TN4Nz7FhuxNzJEmSJEmSJEmS1H5ZypEkSXvXdSAQaNWkHIDszsmNvl/+0LxWPU+SJEmSJEmSJElqyyzlSJKkvUtMgYw+rZ6U858WbNgR1udJkiRJkiRJkiRJbYmlHEmStG+Zua2elANw3mH9G/aPOah7q58nSZIkSZIkSZIktVWWciRJ0r5l5YRlUs6tZ41m9a3TGNgtjVeXbqW6tr712SRJkiRJkiRJkqQ2yFKOJEnat6xBULYFqkrD8riK6joAFn7qElaSJEmSJEmSJElqnyzlSJKkfcvMDW2LV4flcfdcMBaA7eU1YXmeJEmSJEmSJEmS1NZYypEkSfuW9Vkpp/VLWAH0ykgBYPPOyrA8T5IkSZIkSZIkSWprLOVIkqR9+2xSTlF4Sjnd05MB2FJSFZbnSZIkSZIkSZIkSW2NpRxJkrRvqV0hNTNsk3JSEuPpmpbIxh1OypEkSZIkSZIkSVL7ZClHkiQ1T2Zu2CblAPTPTOPR99byesHWsD1TkiRJkiRJkiRJaiss5UiSpObJyg3bpByAzskJAHzvHx+F7ZmSJEmSJEmSJElSW2EpR5IkNU9mLmxfB3U1YXncFZMHA7C1pCosz5MkSZIkSZIkSZLaEks5kiSpebJyIVgHO9aF5XGThnZv2P/VrCVheaYkSZIkSZIkSZLUVljKkSRJzZOZG9oWhW8Jq8/8/tUVYX+mJEmSJEmSJEmSFEuWciRJUvNk7SrlFIevlHPTKSMA6JQUT319EIBpv3mDh+esCds7JEmSJEmSJEmSpFiwlCNJkpqncy9ISAnrpJxLv5TLXV8+hLLqOgZdP5Mn5q1n4ac7ueFfn4TtHZIkSZIkSZIkSVIsWMqRJEnNExcHmTlQvDqsjx3TP7Nh/3v/+Cisz5YkSZIkSZIkSZJixVKOJElqvszcsE7KAeiRnhzW50mSJEmSJEmSJEltgaUcSZLUfFm5ULwKgsGwPbJTcgLXnTSsyfHq2vqwvUOSJEmSJEmSJEmKNks5kiSp+TJzoaYcSjeH9bHfPHowi386lVtOH0WfLikA3P/6irC+Q5IkSZIkSZIkSYomSzmSJKn5snJD2zAvYQWQmhTPReMH8sx3vgRAaVVd2N8hSZIkSZIkSZIkRYulHEmS1HyZu0o5xeEv5XymW+dkuqYlUlpVE7F3SJIkSZIkSZIkSZFmKUeSJDVf1wEQiIvIpJwv2l5ew8Nz1kb0HZIkSZIkSZIkSVIkWcqRJEnNl5AEXfpFdFLOF20pqYzKeyRJkiRJkiRJkqRws5QjSZL2T2ZuxCflpCXFA3D/aysj+h5JkiRJkiRJkiQpUizlSJKk/ZOVG/FJOR//+EQASqtqI/oeSZIkSZIkSZIkKVIs5UiSpP2TmQvl26ByZ8RekRAfR6+MFB57f13E3iFJkiRJkiRJkiRFkqUcSZK0f7JyQ9sIT8vZtLMSgI/Xb6eypo6K6rqIvk+SJEmSJEmSJEkKJ0s5kiRp/2TuKuUURbaUc9mk0HsefW8th//8RYbfNAuALTsr2VFRE9F3S5IkSZIkSZIkSa1lKUeSJO2fKE3Kuf7k4QA8+t46dlbWAlBSWcPhv3iJrz/4fkTfLUmSJEmSJEmSJLWWpRxJkrR/ktMhLTvik3ICgUCTY5c/PA+A91cXU1RWHdH3S5IkSZIkSZIkSa1hKUeSJO2/rNyIT8oB6JQU3+j7W8u3NezfMXtpxN8vSZIkSZIkSZIktZSlHEmStP8yc6FodcRf8+xVk/Z4rrq2PuLvlyRJkiRJkiRJklrKUo4kSdp/Wbmwcz3URnYJqdzsTrs93rdrKpWWciRJkiRJkiRJktSGWcqRJEn7LzMXgvWwfW3EX/WLMw7mhmnD+fe3JzYc27C9gmc++pTKmrqIv1+SJEmSJEmSJElqCUs5kiRp/2XlhrbFqyL+qq8cMYCvTxrEqD5dADh2WI+Gc8NunEUwGIx4BkmSJEmSJEmSJGl/JcQ6gCRJOgBl7irlFEW+lPOZuLgAb/xwMtmdk3l+4Sau+ft8AFZvK9/jMleSJEmSJEmSJElSrDgpR5Ik7b/OPSCxU1Qm5XxR/6w0UpPiOf3Qvg3HXl26JaoZJEmSJEmSJEmSpOawlCNJkvZfIACZOVGdlPOfendJAeDdlUW7PV9ZU8e8Nbs/J0mSJEmSJEmSJEWapRxJktQyWblRn5TzRe9cdxzHDevB8q2luz1/zyvLOev373D9UwuinEySJEmSJEmSJEmylCNJkloqMweKV0N9fcwipCTFs3xLKX98fWWTc6sKywB45N210Y4lSZIkSZIkSZIkWcqRJEktlJULtZVQuilmEU45uDcAP5+5uMm5rE5JAAzrlR7VTJIkSZIkSZIkSRJYypEkSS2VmRvaFjWdUhMtU0f1AmB474wm5/72zhoAKmvqoppJkiRJkiRJkiRJAks5kiSppbIGhbZFq2IWIRAIMHFIN9KS4lm+pYTautBSWjsqahquKS6v2dPtkiRJkiRJkiRJUsRYypEkSS3TpT/EJUBx7Eo5AF1TkyjYXMLxd77O2fe9A8CG4goA0lMS2FlZQ119MJYRJUmSJEmSJEmS1AFZypEkSS0TnxAq5sRwUg5ARmoiJZW1AMxft51Pt1ewfGspAMcc1INgsPHkHEmSJEmSJEmSJCkaEmIdQJIkHcCycmM+KadLamKj7xNufblhPze7EwDF5dVkdUqKai5JkiRJkiRJkiR1bE7KkSRJLZeZG/NJOV3TEvd4bnD3UCln847KaMWRJEmSJEmSJEmSAEs5kiSpNbJyoXI7VBTHLEJ8ILDHcxMGZwOweFMJ9fVBCkurohVLkiRJkiRJkiRJHZylHEmS1HLdhoS2mxfFLMK00b0ByO6cxEE90xuOnzK6N93TkwG45dlFfOexD8n/2YvsqKiJSU5JkiRJkiRJkiR1LJZyJElSyw2cAHEJsPyFmEXo0zWV1bdOY+4NJ/DgpYc3HB/drwsAQ3p0BmDGxxsBOOPet6IfUpIkSZIkSZIkSR2OpRxJktRyKV1CxZyC52OdBIAeuybjAIzpnwnA90/Ma3TNyq1lLN1UEtVckiRJkiRJkiRJ6ngs5UiSpNbJmwpbFkHxmlgnIS4u0LA/qm8GAEcOym5y3U1PfxK1TJIkSZIkSZIkSeqYLOVIkqTWyZsa2i6bHdscu/zp4ny+PXkIaUkJAHRJS2TGVV8C4LCc0PScd1cVUVVbF7OMkiRJkiRJkiRJav8s5UiSpNbpNhi6DYGCWbFOAsDxI3ry/SkHNTo2sk8XZl41ib9ecnjDsYNuaBt5JUmSJEmSJEmS1D5ZypEkSa2XNxVWvQ5VpbFOskcj+mTQOTmBm08b2XBsxda2m1eSJEmSJEmSJEkHNks5kiSp9fKmQF01rHot1kn26asTchr2T//dW7ELIkmSJEmSJEmSpHbNUo4kSWq9AUdCckabWcKqufpmpsY6giRJkiRJkiRJktopSzmSJKn14hNh8LFQMBvq62OdZp8+uXkK4wZmsmRTCTV1TfPOWbmNnGtnMPHWl6mqrYtBQkmSJEmSJEmSJB3oLOVIkqTwyJsKpZtg00exTrJPnZMTmDQ0G4CH56xh1icbG869v7qI8+6fA8CG7RU8+cGGmGSUJEmSJEmSJEnSgc1SjiRJCo+hJwABKHg+1kma5dz8/gDc/MwiLn/4A+56oQCAc+57p9F11z25gILNJVHPJ0mSJEmSJEmSpAObpRxJkhQenbKh32FQMCvWSZqlW+ekRt/vfmkZ9fXB3V5732srohFJkiRJkiRJkiRJ7YilHEmSFD55U+DTD6FkU6yT7FNyQjyH52Y1Ojbo+pkN+y/+z1Ec1DMdgCc/2MD8ddujmk+SJEmSJEmSJEkHNks5kiQpfPKmhrbLZsc2RzM9/s0jWX3rtCbH7/nKWIb0SOe5qyc1HHtt6dZoRpMkSZIkSZIkSdIBzlKOJEkKn54jIaMfFDwf6yStcsxB3QGIiwtw9rh+ACQl+NcmSZIkSZIkSZIkNZ//uiRJksInEAgtYbXiFaitinWaZrv7vDEAWkClUAAAIABJREFU5HRLY8ktU+mUnNBw7razR5OUEMcHa4upqK6LVURJkiRJkiRJkiQdYCzlSJKk8MqbCjVlsPrNWCdptv8a05cXvnsUM6+eREpifKNzgUCALqmJvLBoM8NvmmUxR5IkSZIkSZIkSc1iKUeSJIVX7iRISD3glrAa2jOdtKSE3Z7bWvL51J/hN82KViRJkiRJkiRJkiQdwCzlSJKk8EpMhUHHQMEsCAZjnSYqKmucniNJkiRJkiRJkqTGLOVIkqTwy5sC29fA1qWxThIWs797FLf818iG7x+uLaauPlQ4GnL9TIbdOIvH3lsbq3iSJEmSJEmSJElqgyzlSJKk8MubEtoWtI+lnvJ6pnPRkTlcd9IwAM64923Ov38OH63bTu2ucs61Ty5gwfodrN1WHsuokiRJkiRJkiRJaiMs5UiSpPDL6AO9RkPB87FOEla9u6Y27L+3uojv/n1+o/On/u5NjrrtlWjHkiRJkiRJkiRJUhtkKUeSJEVG3hRYNwfKi2KdJGxOHNGz0feVhWW7va5+1/QcSZIkSZIkSZIkdVyWciRJUmTkTYVgPSx/KdZJwiYlMZ4lt0xtdGxIj84s+umURsfmrimOZixJkiRJkiRJkiS1QZZyJElSZPQZC2nZUDAr1knCKiUxnv8a06fhe2llLWlJCSz7+UkcPzw0SeebD81tct8Ha4vZVlrFgvU7opZVkiRJkiRJkiRJsZMQ6wCSJKmdiosLLWG15Fmoq4X49vPXjrvOHcO4gZnc//pK7jjnEAAS4+O494Kx5N3wHMXlNdTVB1lXVM764goGZKVx5r1vN9z/0Y9PpEtqYqziS5IkSZIkSZIkKQraz7+OSZKktidvCsyfDuvehZyJsU4TNnFxAS4+MoeLj8xpdDwpIY5JQ7N5Y1kh28urOeb2V3d7/5/fXMX/nJAX+aCSJEmSJEmSJEmKGZevkiRJkTNoMsQltrslrPbmhBGhJayOveO1PV7zm5eWRSuOJEmSJEmSJEmSYsRSjiRJipyUjNCEnILnY50kasqr6wDYUVET4ySSJEmSJEmSJEmKJUs5kiQpsvKmQuFSKFoV6yRR8d8Tchp9X3jzFFbfOo0FPzmRO845pOH42ysKo5xMkiRJkiRJkiRJ0RSxUk4gEOgfCAReCQQCiwOBwMJAIHB1pN4lSZLasLwpoe2y2bHNESUpifEN+5cfPZhOyQkApKckcta4flxxzGAAvvLHd2OST5IkSZIkSZIkSdERyUk5tcD3gsHgcGA8cGUgEBgRwfdJkqS2KGsQZOdBwaxYJ4mad647ljPH9uWa44c2OfftY4cAMCi7U7RjSZIkSZIkSZIkKYoiVsoJBoMbg8HgB7v2S4DFQN9IvU+SJLVheVNg9ZtQVRLrJFHRu0sqd547ptHUnM+kJSVwSL8urCwsA+DYO14l59oZ1NcHox1TkiRJkiRJkiRJERTJSTkNAoFADnAo0GSdhkAg8I1AIDA3EAjM3bp1azTiSJKkaMubCnXVsPLVWCdpEz5avwOAnGtnsHJrqJyzpqg8lpEkSZIkSZIkSZIUZhEv5QQCgc7AE8A1wWBw53+eDwaD9weDwfxgMJjfvXv3SMeRJEmx0P8ISM2E134F5UWxThNzV+1awuqL/u/FghgkkSRJkiRJkiRJUqREtJQTCAQSCRVypgeDwScj+S5JktSGxSfCGffD1qXwt9M6fDFnS0lVk2NPz/+UNdvKYpBGkiRJkiRJkiRJkRCxUk4gEAgAfwYWB4PBOyP1HkmSdIDIOxHOexS2FsCDp0HZtlgnipnrpw3f7fGjb3s1ukEkSZIkSZIkSZIUMZGclDMRuAg4NhAIzN/1OTmC75MkSW3d0OPh/Edg27LQxJyywlgniomMlET+eHE+T14xgRW/OJlz8/sBkJ6SEONkkiRJkiRJkiRJCpeIlXKCweCbwWAwEAwGRweDwTG7PjMj9T5JknSAGHI8nP8obFsOD54KpVtjnSgmThjRk7EDMomPC3DzaaMYPyiLjJTERtfU1Qd5YdFmauvqY5RSkiRJkiRJkiRJLRXJSTmSJEm7N/hY+MrfoWjVrmLOllgniqnUpHgOH5hJrx3zqanY2XD8sr/N5bK/zeVXs5bEMJ0kSZIkSZIkSZJawlKOJEmKjUHHwAWPQ/FqeOAUKNkc40AxVLKZU5f8gCeSb2bdnceyoGAF20qreHlJqKy0YMOOGAeUJEmSJEmSJEnS/rKUI0mSYif3KLjwn7BjHTx4CpRsinWi6PvkSbh3PIN3vstfa6fQu3oNaQ9PY83KpQ2XzFlZxENz1nDmvW/xj7nrGt1eU1fv8laSJEmSJEmSJEltUCAYDMY6Q4P8/Pzg3LlzYx1DkiRF2+q3YPo5kNEbvvpsaLs7VaWwZRFs/gQ2L4TCZdB7NIw4A/qOhUAgurlbo2wbzPweLHwK+oyFM+4j547l5AeW8Jek2yklhYuqr2NFsG+TW6eM7MkvzjiY4+98jeLyGob1SmfWNUfF4EdIkiRJkiRJkiR1PIFAYF4wGMzf53WWciRJUpuw5h2YfjZ07glffQbqqkLFm80LYdOC0LZ41efXJ2dAZg5sWQz1NZDRD0b8V+jT7zCIa8MDAZfMgGeugYpiOOZamHgNxCcw5a7X2VlZQ5edBTyU9EviqWPZCQ/w5Wer9/nIRy47ggmDsyOfXZIkSZIkSZIkqYOzlCNJkg48a9+Fh8+C6pIvHAxAtyHQcyT0HLVrOxK6DghNxqkohqWzYNHTsOIlqKuG9N4w/LRQQWfAeIiLj9lPaqRiO8y6Fj56FHodDKffB71GNZzeWVlDXCDAqB8/z4DAZh5O/AUDUiqYOfJ2rngnY5+PX33rNFYXlnHM7a8y86pJjOiz73skSZIkSZIkSZK0fyzlSJKkA9On82Hhk58XcboPh6S05t1buRMKnodF/4LlL0JtJXTqAcNPhQFHQnJnSOoEiZ1Cz2zY7wQJyZFd/mr5i/D0d6B0M0z6Hhz1A0hI2u2l97yynJeXbOGuk3oyYOaF1Bcu41uVV7K822RmXj2Jg388m+q6egCmjuzFrIWbmjxj4pBuTP/6+Mj9HkmSJEmSJEmSpA7KUo4kSerYqkph2ezQBJ1ls6GmfO/XB+IgqTMkp0PWIMgeCtl50G1oaL9L//1bEqumMlTAKd0M86fDvAeg+zA4/ffQd2zzn1NeRMWDZ5O06QNm5FzLaZdcC8BvXlrGnS8U8NGPT+SQm2fv9tbVt05r/nskSZIkSZIkSZLULJZyJEmSPlNdDjvWQXVZqJxTXfb5p6YcqktD19SUh5bD2rYCCgugcvvnz0hIDU3v+ayskz00dLx0M5Rsarr94r0EYOJVcMz1kJjSgvxl7HzwfDI2vAYn/BQmXt3o9MtLNnPpA03/DvXtyUP4/pSD9v99kiRJkiRJkiRJ2iNLOZIkSa0RDEL5tlA5p7AACpd9vr99LQTrP782PhnSe0LnXrvZ9gqVebJyW5enthqe+mZoaa+J18DxP2m03NavZi3h2Y8/ZV1RBUkJcVTXhvLNumYSw3pltO7dkiRJkiRJkiRJamApR5IkKVJqKqFoZWjJq/SekNK1UUEmYurrYOb3Ye5fYPSX4aRfQWpmw+nq2noWbNjOiN5dGH7TLAAmDunGr84aTb/MtIbrJv36ZdYVVfDYN8YzflC3yOeWJEmSJEmSJElqR5pbyomLRhhJkqR2JTEFeo6AHsNCpZhoFHIA4uJh2p2hZbAW/BN+dxh89PfQVB8gKSGOcQOzSE2K5+xx/QB4a/k2jrntVWrrQpNzvvnQXNYVVQBw3v1zuPgv7xEMBqmuraeypi46v0OSJEmSJEmSJKkDcFKOJEnSgWjjxzDjf2D9+5AzKVTW6Z7X6JKca2c0+n5UXndeL9ja5FGvfP8YLv7LuxSWVLP4lqkRjS1JkiRJkiRJknSgc1KOJElSe9Z7NFw6G075P9i0AH4/AV66BWoqGi75+RmjGt2yu0IOwLLNJawrqqCipo7KmjqCwSDPfPQp97yyPKI/QZIkSZIkSZIkqT2zlCNJknSgiouD/Evg23Ph4LPhjdvh3vGw7AUALjhiIKtvncbhOVmNbuuflcp7Pzqu4fvKwrKG/YLNJfxsxmK+8+iH3Pb8Uj7ZsCM6v0WSJEmSJEmSJKmdsZQjSZJ0oOvcHc64D776LMQnwfSz4fGLYeenADx++ZEMyu7UcPkbPzyWHukpzLkuVMz585urGs6t3lbe6Pucldui9CMkSZIkSZIkSZLal4RYB5AkSVKY5E6Cy9+Ct38Dr98Gy1+Co74P3Yfx0mlx/GLWSg7pnwUrX4NAHL3i4jk6dSU7S+voHYijiAyueXQe6clJlFTVArBxR2WMf5QkSZIkSZIkSdKBKRAMBmOdoUF+fn5w7ty5sY4hSZJ04CtaBc/9EJbN3q/bqoIJrA32pKZrLvNKslhc3Z2ff+00At2GQHqf0JJZkiRJkiRJkiRJHVggEJgXDAbz93mdpRxJkqR2KhiEwmVQXRraD9ZBfR0E6xv2N24v47onPiKeOroHdpAT2ERuYBMHpxbSrWoDyYGaz5+XkApZudBzFORfyj+39qOovJpvHDU4dr9RkiRJkiRJkiQpyppbynH5KkmSpPYqEIDueXu9pDfwi8HH8VrBVs4a24+8G54D4PfnjuWK6XPpTRE5cZt45IxsKFoJ21bA8hdgweMMqR/MH2unUT3+RpKSkqLwgyRJkiRJkiRJkg4cTsqRJElSg007KnlpyWYuOGIgqwrLmHz7qwC8/6Pj6Z6eDMCGLYX8/u6f8bX4meTGbaY0tQ+dj/oOjL0IktNjmF6SJEmSJEmSJCnymjspJy4aYSRJknRg6NUlhQuOGAhAbnYnpn/9CAAKNpc0XDNvYzUP153AcdV38I3q77KoLB2evw7uHAmzb4QdGyITrmQzLJkBOzdG5vmSJEmSJEmSJElh5PJVkiRJ2qO8nqHJN0s2lTBxSDZPz9/ALc8uBqCeOGbXH8bs6sNY9M1s0ub+Ht75Hcy5F0aeCRO+Db0PaV2AbStgybOhMs6694BdUx77HQ7DTw19snJb9w5JkiRJkiRJkqQIsJQjSZKkPeqenkzn5ATWF5dTXFbN1Y/Nb3T++OE9eHHxFhbH5THunAegeA28ex988DdY8Dik9yHYcxSFnYfSfcg46DUasgZBXPzuXxgMwsb5sHhXEWdrqABEr9FwzHUw8EhY+y4s/je8cGPo0+tgGH5aqKDTfRgEApH9Q5EkSZIkSZIkSWqGQDAYjHWGBvn5+cG5c+fGOoYkSZK+YPLtr7KqsKzJ8RNH9ORbxwzmjHvfBmD1rdM+P1mxHT5+HDbMpXTNhyRvX0FioA6AYGIawe7Dies1KlSo6XUw1FaFSjhLZsDO9RCIg4ETYdgpMOxk6DqgabDi1aHyzuJ/w7p3Q8e6Dfl8gk63oVBTDtVlu7blUFO2a/uF4/FJoevTe4X7j06SJEmSJEmSJLVDgUBgXjAYzN/ndZZyJEmStDdjfjqb7eU1TY6vvnUaOytrGP2T2Q3HjsjN4pHLxhMfFyAYDDJnZRHrisu54Z/zGBrYwIxzu/LnJ55hZNwaxqd9CpXbP39gQioMOQ6GTYOhU6BTt+aHLNkUWuZq8TOw6g0I1u3fjwzEw9ATYexFoW184v7dL0mSJEmSJEmSOgxLOZIkSQqLnGtnNOxfcMQAPtmwg5tOHcm4gZlNzn9m9a3TeHzuOn74z4/3+NwnLh/PuMwK2PQJEITcoyEprfWBy4ug4HkoL4TENEjqtGubBomd/mObBmVb4cOH4aNHoXQzdO4Jh5wHh14E2UNbn0eSJEmSJEmSJLUrlnIkSZIUFm8tL+SCP73L0B6dmf3dowgEAo3O3/b8Eu55ZUWjYx/9+EQOuXk2e3PzaSP56oSccMdtubpaWDYbPnwoVOoJ1sGAI0PlnJGnh8o90VJfB1U7oXIHVO7adhsMGX2il0GSJEmSJEmSJO2WpRxJkiSFTV19kLgATQo5ALV19Qz50XOkJydwzQl53PLsor0+K6tTEkVl1Zx2SB9+c/6hkYrcOiWbQpNzPngIilZAUjqMOhNGnQUDJ7R+eauqElj2Aqx4GcoKd5Vvdn2qdoY+/yk+CfIvhUnfh87dW/d+SZIkSZIkSZLUYs0t5SREI4wkSZIObPFxTcs4n0mIj+N3XzmUkX26sLqwbI/Xff/EPA7u15VJQ7IZ89PZVNXWRSJqeKT3gi99FyZeA2vfCZVzFvwDPngQkrvAkOPgoJNgyPGQltW8Z5Ztg6UzYcmzsOIVqKuC1Ezo0g9SukJWLqR0CX2SMz7fT+kSmtKz8El474+hLEdeARO+EzonSZIkSZIkSZLaJEs5kiRJarVTRoeWVcrunNTo+N3njWFE7wyG9kxvdPzQAZls2F4RtXwtFgiEJuMMnAAn3wYrX4WC50LLWy18EgLxMGB8qKCTdxJkD2l8/471sGQGLH4G1rwFwXroMgAO+xoMPxX6HwFx8c3LMngyTLgKXvk5vH4bvP+nUHHo8G9AYmrYf7okSZIkSZIkSWodl6+SJElSWBWVVTP2lhcAuOOcQzhrXL8m11z/1AJmfbKJD248IdrxwqO+Hj79AJY+F/psWRg63m0I5E0NTcBZMiN0DUD3YaESzrBToPchobJPa3w6H16+BZa/COm94egfwqEX7X1ZrapS2PRx6N6N82HLIhhyAhxzHSQk7fk+SZIkSZIkSZLUiMtXSZIkKSayOiVx5eTB3PPKCsprdr9EVb/MVIrKqtlZWUNGyl6KJG1VXBz0yw99jrsRtq+FpbNCU3Teux/qqqHPWDjux6EyTvbQ8L6/zxi48AlY/Ra8dDM8+114+7cw+Ucw8kyoKW9cwPl0PhQWALsK+em9oesAePNOWP4CnPkn6DEsvBklSZIkSZIkSergnJQjSZKksCuvruV3Ly/nquOGkpLYdHmmGR9v5MpHPuDZ73yJUX27xCBhBFWVQHU5pPeMzvuCwdByWi/fAps/gU7doayQRgWc3mNCRZ7Ptum9QueWzIB/fweqy+CEn4aWwmrtFB9JkiRJkiRJktq55k7KsZQjSZKkqHt35Ta+fP8cAFbfOm2v15ZV1fLeqiKOOag7AQsje1ZfDwufhKUzITuvaQFnT0o2w9NXhibmDD4OTr933/dIkiRJkiRJktSBuXyVJEmS2qweGSkN+5U1daQkxnPl9A+Yv247I/tkcOeXx9A5OfRX1d+8tIw/vL6SKSN7ct+F4yzm7ElcHBx8duizP9J7wgX/gPf/BLNvhHuPhFPvhhGnRSanJEmSJEmSJEkdhJNyJEmSFHXBYJDc62Y2fP/H5Udyzn3vNLpm3MBM5q0ppm/XVDZsrwDg8W8eyUG90nmtYCunju5tQSfcthbAk5fBxvkw5kI46VZITm/+/cGgy19JkiRJkiRJkto9l6+SJElSmzZ/3XZOv+ctABLjA9TU7d/fS/915UTG9O8aiWgdW201vPYrePNO6DoAzrgfBhzx+fnqMihaBcWroGhlaL9oZeh7eTGceAvkXxK7/JIkSZIkSZIkRZjLV0mSJKlN+2KhZl+FnPyBmcxdU9zo2NWPfcgvzjiYUX27UFRWTXbnJNJTEiOStUNJSILjboQhx8NT34C/ToWDTobybaHyTenmxtenZkHWIOg/Hnash2evgfhEOPTC2OSXJEmSJEmSJKmNsJQjSZKkmBnRO4NFG3fu87rvnpDHBX96t9GxNdvKGx3r2zWVt649FoC6+iDby6tZsbWMw3IyXeaqJQYeCZe/Bc9fDytegcyBMOQEyMoNlXCyciEzF1K/MK2ophIeOx+e/jbEJ8Poc2KXX5IkSZIkSZKkGHP5KkmSJMVMZU0dX39wLm8uLwTgB1MOIrtzEv/7xAIA7vnKWAIBOPng3hSVVTN9zhouO2oQw26ctdvnrfrlyQQCAX701AKmv7sWgF+fNZpzD+sfnR8kqC6HR86FNW/D2X+BkafHOpEkSZIkSZIkSWHV3OWr4qIRRpIkSdqdlMR47vnKWAZld+L3F4zlyslD+PJhAxrOn3xwL04+uDcAWZ2S+M5xQ0lJjOfu88ZwUM/0Js/LvW4ml/z1vYZCDsD64vLI/xB9LikNzn8M+h0GT3wNlsyMdSJJkiRJkiRJkmLCSTmSJElqc576cD2lVXVcNH7gXq+b+n+vs2RTyV6vyc3uxKOXjadHejJxcS5jFTWVO+Gh02HTAjjvURh6fKwTSZIkSZIkSZIUFk7KkSRJ0gHrjEP77bOQA3DermWp5t7QtPBx+zmHALCqsIzxv3yJe19dHt6Q2ruUDLjwCeg+DP5+Aax8NdaJJEmSJEmSJEmKKks5kiRJOmB9dUIOy39+Etmdk7nimMENx8cO6MrZ4/pxWE5mw7HH3l8Xi4gdW2omXPQvyBoMj54Pa96OdSJJkiRJkiRJkqLGUo4kSZIOWIFAgIT40F9pfzh1GHd9OTQdp1NyAgDHDe/ZcG1dfdtZtrVD6dQNLv4XdOkH08+Bde/FOpEkSZIkSZIkSVFhKUeSJEntxulj+vLjU0c0LF3VPzMNgJTEODbuqOQvb64CIBgMMu03b5Bz7QzLOtHQuQdc/O/Q9uGzYMMHsU4kSZIkSZIkSVLEWcqRJElSuxEIBLhkYi49M1IAOPngXtx34Vj+8c0JAPz02UXU1wf5YG0xCz/dCcAV0+fFLG+HktEbvvoMpHaFh86AtXMgaCFKkiRJkiRJktR+BYJt6H8Iz8/PD86dOzfWMSRJktQOnXPf27y/uni35x657AgmDM6OcqIOqng1/HUa7FwPmTkw7JTQp//hEBcf63SSJEmSJEmSJO1TIBCYFwwG8/d1nZNyJEmS1CEclpO1x3NzVmzb7fGK6jrWFZVHKlLHlJkDl78Bp/wfdBsK790Pf50Kt+fB09+GpbOgpjLWKSVJkiRJkiRJarWEWAeQJEmSoqFHenKTY2/+72S+9KtXWLq5hGAwSCAQ4NmPP+Xbj3wIQHxcgLr6IEtumUpKolNcwiYtC/IvCX0qd8LyF2HJs7DwX/DhQ5DYCYYeH5qgM/QESM2MdWJJkiRJkiRJkvabpRxJkiR1CBcdmcPo/l0ZOyCTnGtnANAvM41D+nXh+YWb+a973uJfV0xsKOQA1NWHlnr9ZMMO8nOyqKsPEh8XiEn+dislA0adGfrUVsHqN2DJDFgyExY9DQkpMOl7MPFqSGharJIkSZIkSZIkqa1y+SpJkiR1CPFxAcYOCE1cWfqzqSy5ZWroRCBUsvl4/Q5mLdy023u/9uBcnv34U/JueI4tO11aKWISkmHI8XDKXfA/i+HrL0HeVHjl53Dfl2D1m7FOKEmSJEmSJElSs1nKkSRJUoeTnBDfsBzVof27Nhx/6sMNnDCiJ8N7ZzD7u0c1HN9RUcP//vNj6uqD3DG7IOp5O6S4OOiXD+c+CBf8MzRF54Fp8K8roGxbrNNJkiRJkiRJkrRPgWAwGOsMDfLz84Nz586NdQxJkiR1ICWVNdwxu4AH3l7dcGxknwxmXDWJ2rp6jr7tVTZsr2hyX5fURObfdAKBgMtZRUV1Obx+G7z9G0jOgBNvgTEXNEw6kiRJkiRJkiQpWgKBwLxgMJi/r+uclCNJkqQOLT0lkZ+cNpJTRvduODa0R2cAEuLjeOqKCbu9b0dFDVtKqqKSUUBSGhz/Y7j8Teh+EDx9ZWhyztalsU4m7VlNJRQuj3UKSZIkSZIkSTFiKUeSJEkCfnb6qIb9Lx82oGG/R0bKbq8BmPTrV1hVWMYV0+eRc+0MRtw0K/JBO7oew+G/Z8Jpv4XNC+H3E+GlW6Cm6TSjqKmrDS2vJf2n9/8I9xwOhctinUSSJEmSJElSDFjKkSRJkoCuaUkN+7nZnRqde/N/J/PS947mwvEDef0Hk0lLigeguraeybe/yswFmwAor66jsqYueqE7qrg4GHsxfHsujDoL3rgd7j0S1s6Jfpbt6+De8fDXk6He/+z1HzYtgGAdvH57rJNIkiRJkiRJigFLOZIkSdIuL3/vaH599mh6dUlpdLxfZhqDu4eWtBrQLY0FP5myx2ds3FHJ4o07qasPRjSrgM7d4cw/wFefCX1/8DRY9HT03l+4DP4yFbavgQ1z4cOHo/fu/VVVCtPPgfXzYp2kYyksCG0XPA7bVsQ2iyRJkiRJkqSos5QjSZIk7TKoe2fOze+/z+vi4wK8d/1xfPTjE5ucm3z7q5x09xv84fXW/QP8X99axbLNJa16RoeRexRc9jL0PgQe/yq8e3/k37nxo1Ahp7YSvv4iDDgSXvopVO6I/LtbYtnzsGw2vHRzrJN0HMFgqLg14nSIT4I37oh1IkmSJEmSJElRZilHkiRJaoEeGSl0SU1k9a3TePDSw1n806mNzv961lLqWzgtZ1tpFTc/s4hLHng/HFE7hrQsuPhpOOgkeO4H8OJPQqWISFjzDjxwCiSkwKWzQmWgqb+E8m3w+m2ReWdrLd41TWjVa/Dph7HN0lHs/BSqSyHnS5B/KXz0GBStinUqSZIkSZIkSVFkKUeSJElqpaPzupOaFM+lE3MbHV9ZWNai581bUwzA+uKKVmfrUJLS4NyHYNwl8OZd8K9vQV1NeN+x7EV46Azo3CNUyMkeGjre51A49AKYc1/bW6aophIKZsPIMyE5A966O9aJOobPlq7KzoMJV0FcgtNyJEmSJEmSpA7GUo4kSZIUJjeeMpyCn53E4O6dALhy+gd7vf6Rd9eyfEspO8prWL6ltOH4Nx6a17A/d3VRZMK2V/EJcMpdMPlH8NGj8Mi5UBWmZcAWPgWPngfZQ+CSWdD1P5Y6O/am0PSc538UnveFy8pXoKYsVBrKvwQWPQ1FK2Odqv0rXBbaZudBRm8Y99+h/04Wr4lpLEmSJEmSJEnRYylHkiRJCpNAIEBSQhzPXX0UAEs377kM8qtZS7j+qQW+jNc2AAAgAElEQVScfPcbHPLT2Rx/52uUVdU2ue7s+96hsqYuYpnbpUAAjv4hnPZbWPkaPDANSre07pnzHoR/Xgp9x8FXn4XO3Ztek94Tjvo+FDwHy19s3fvCafEzkNwFco6CI74Vmtjy9u9inar9KywITSZK7xX6PvFqCMTBm3fGNpckSZIkSZKkqLGUI0mSJIVZUkIc/z0hh6T4OGrr6nd7zdqicgCqv3D+nPve4d5Xlze59otTdLQfxl4M5z8amljyp+NbvqzU27+FZ66CQZPhoqcgteuerx3/LcjMhVnXh3/prJaoq4GlM+GgqZCQFJrYMvrLMH86lG6Ndbr2rbAgtLxZIBD63qUvHHoRfDgdtq+LbTZJkiRJkiRJUWEpR5IkSYqAQwd0pbqunpPufoPFG3cy6dcvM29NMQBvLNvKjI83Nrln0cad/HrWUgBSE+Mbjm/YXhGd0O1R3pTQZJvqUvjzCbB+3r7v+UwwCC/dArNvgBGnw/mPQVLa3u9JSIYpP4fCpTD3L63LHg5r3oKKYhh+6ufHJl4NtVXw3h9il6sjKCwILV31RV/6bmj75l3RzyNJkiRJkiQp6hJiHUCSJElqj/JzsgBYtqWUW59bwrqiCn7/6nLuu3AcF/35vX3e/+FNJ/Dyki1cMf0DNu+sjHTc9q3fOLh0Njx8Jjx4Ckz9JXTuCfV1EKyDYP2u/frPj9XXwbp3QxNlDr0ITr0b4uL3/S6Ag06GQcfAK7+Ag8+BtKxI/rq9W/wMJKTC4OM+P5Y9FIZNg/f+CBOvgeTOkc+xYwOkdInOu9qCyp1QsjH0Z/1FXfvDoRfAhw/BpO+FpudIkiRJkiRJareclCNJkiRFQN+uqQ37by0vBODFxVsY8qPnGo7fcvqohv2ZV01iTP/QskjXHD+UlMR4ThzRE4Cbnl7Y5PnXPbmAw3/+IvX1wYjkb3eyh8DXXwyVJJ65Gh49D/5+ATx+Mfzjv+GJr8GTl8G/LoenrwwtVzV/Okz4Dpz22+YXciC0XNGUX0JVSaiYEyv19bD4WRh6fNMJPxOvgcrt8MHfIp+jphL+MAn+dFxoak9HsG1ZaPufk3IAvvQ/oQLYW3dHN5MkSZIkSZKkqHNSjiRJkhQhr/9gMkfd9gq1uynO3HrmwZx3+AAIBumRkcKIPhn84aJxXPvEx1x8ZA4ACfGfd+h3VNTQJTWx4fuj760FYMmmEkb0yYjsD2kvOveAr70Amz8BAqGiTSAeAnGf78fF7drGhybMdOrWsnf1HAH5l8LcP0P+JdBzZFh/SrNsmAulm2D4aU3P9T8MBk6Ed+6Bwy+D+MSm14TL0plQvg3Ki+DRr8BFT0FiSuTe1xYUflbKOajpucyBcMj5MO+B0HJWGb2jGk2SJEmSJElS9DgpR5IkSYqQAd3S9nhuVN8uAFx0ZA5TRvYCoGdGCn+95HCyOiU1XHf6mD4AHHLzbHaU11BRXcfj769rOL9he0UkordfCcnQdxz0HQu9D4Feo0IFmu4HhabpZA0KlSa69Gt5Ieczk6+H5AyYdR0EYzDRaPG/IS4Rhp64+/MTr4ad6+GTJyKbY/4jkNEPzvwjrH37/9m77/Aoy6yP499JL4Q0agqEUAKh19CbSlewo7L2gmV1be+ytlVxXVzrunbFLhZUUJpKUek9oUNoCSWhBEgIgfR5/7hJIKROMjMp/D7XxfXMcz93ORNCLnEO58CPd5r2YHXZ0R3g4gZBLUp+PuBRyM+F5W86Ny57qI7vZRERERERERERkVpKSTkiIiIiIg404mzCzfJJQ5n30AAWPz6E7+7pU5iUU57XrutS+Lrz87/R7plfeHPRzsKxQ2lKyqmxfIJMYs7eP021GGeyWmHbLIgcBN4BJc9pPQwaRZs2So5KtDiZDLsXQufx0Ola09Zr2yyY9/e6ndyREg+BLUqvQBTUwnxN1n4M6YcdE0N+Puycb98EqNiv4KUIU+WnLv/+iYiIiIiIiIiI2ImSckREREREHOiN8V1Y9OggQgK8ade0Ps2CfejVIqjC611cLHi6Ff3P9gMnztDQzxNXFwvJaZn2Dlnsqcft0LAt/Pok5GY579zDm+FEArS7vPQ5Fgv0fRCObDXJG46w8Vuw5kOXG819n/ug719hzYew9DXHnFkTpOyEBm3KnjPgUcjLdly1nN2L4KtrYM1U++yXkwmLJkPOGZj1EEy7DtIP2WdvERERERERERGROkpJOSIiIiIiDuTl7kpkw3pV2mPHCyMJPq+lFUDHUH/8vd2Zvu5AlfYWB3N1h+Evwom9sPJd5527bRZggajRZc/reI1pLbXsDfvHYLXChq8hvDcEtzw3funz0PE6WPi8aW1V1+TlwPE90LCcpJzgltDxWpM0c+qo/eM4uM5cl7xqEmmqKvYLSE+GG7+FES/B3sXwTm/YMqPqe4uIiIiIiIiIiNRRSsoREREREakFfnt4IBN6Nyu8j2kRRKCPO0fTs9h37HQ1RiblanUJtBkJi192XKuiC22bBc37Qr2GZc9zdTfVaxKXwf419o0haT0c3X6uSk4BFxcY+zZEDoafHoCdC+x7bnU7kQD5OeVXygEY+DjkZsKK/9k/juQ48KgHpw5VvVpObhYseQ2a9TG/b70nwj1LIDACpt8KP9wJZ05UPWYREREREREREZE6Rkk5IiIiIiK1QHA9T14Y15Eu4QEA9GvVoLACz+crEqovMKmY4f8yiQ0Ln3f8WSm7TEuqslpXna/bLeAVYP9qOXHTwM0L2o8r/szNA677AhpHw3c3n6vqUhekxJtrRZJyGrSGDlfD6o8g45h940iKg6iREDnEtArLOlX5vdZ/DulJMHiSaXsGphLQHfNh8BOmWs47fWHXQvvELiIiIiIiIiIiUkcoKUdEREREpBb58d6+LH58CB1C/fnn5dEAfLkqkbQzOXR9/jcWxzugDY5UXXBLU10k7itY8bZp7eQo22eZa9sxFZvvWQ963gnb50DKTvvEkJMJm743iUFe/iXP8aoPN/0AvsHw1XVwbLd9zq5uhUk5rSs2f+DjkHMaVrxlvxhOHTFJNCFdYehTcPoYrH6/cnvlZsHS100bshaDij5zdYfBfzfJOZ5+8OVVMOdRyM6o+nsQERERERERERGpA5SUIyIiIiJSi7i4WGgW7ANAiL83AJk5+Yx8YzEnTudw88erWbXHzhU3xD4G/wPajoZfn4Af74JsB7Ud2zYLQrpBQHjF18RMBDdPWP6mfWKInweZqcVbV13IrzFMmAHWfPjyajhVB5LKUnZCvSalJyNdqFFbU01o9Qdw+rh9YkiKM9emXSCsB7QZAcv+C2dSbd8r9gs4edAk3xRUyblQaDe450/ofb9plfVef9i/uvLxi4iIiIiIiIiI1BFKyhERERERqaVcXCx4uJn/pE9Kyywcv/6DlfwUd7C6wpLSePialk1DnzJVZKYOgxMJ9j0j7YBpBVXR1lUF6jWELjfBhm8g/VDV44ibBvVDi1dWKUmDVnDTdHPutGur1mapJkiJr3iVnAIDH4fsU+brZg/JcYAFmnYy90OegMw0WPmObfvkZsGS1yE8xrTBKou7N4x4EW6ZBXk58PFwU31JRERERERERETkIqakHBERERGRWuz9Cd1LHH/omzgiJs1h+6GTRcZTT2cTMWkOczYmOyM8uZCLi0nAuGk6pO2DDwbDroX2278gCaLdFbav7fsA5OfCynerFkP6Idi1ADqPBxfXiq0J6wHXfgrJG+G7myE3u2oxVBerFY7GQ4M2tq1r3B4CW8C+FfaJIykOgluZllIATTub74kV79hWjSf2Szh5AAaVUSXnQi0GwL3LoX4YrPvU5tBFRERERERERETqEiXliIiIiIjUYkPaNuKO/i2Iblqf+BdGFns+4o0l/PXrWNLO5ADw8q87ALh/2nqnxikXaH0Z3PU7+IXAV9fA0tdNQkdVbZsFDduZ6jO2CoqE6LGw9mNTVaWyNn5n2lF1Lqd11YWiRsDlb8DuhfDD7ZCXW/kYqsupI5CVZntSDphqNPtX2+f7IDkOQroUHRvyhKnGs+y/FdsjNxuWvAZhvaDlUNvO96pvqjXt+QOy0m1bKyIiIiIiIiIiUocoKUdEREREpJZ7ekw0cx8agIebC3HPXEb/Vg2KPJ+1IYnOz/1G7L4TbEk6VzknNy/f2aHK+YJbwp3zIXocLHgWpt9StdZNGSmQuMz21lXn6/cQZJ2sfIUTq9W0YAqPqVxiULebYfi/TXLRzHshP69ycVSXlHhztbV9FUB4L8g4UvWWZqeOwMmD0PSCpJxG7aDjNbD6AzOnPHFnq+QMtqFKzvnajoa8bPtWghIREREREREREalllJQjIiIiIlKHBPh48OWdMcz+a/9izz5bnkDc/tTC+1ZPznNmaFISD1+45mO4bLJJRPnoUji2u3J77ZhrKtRUJSknpCtEDoHFr1YuOSQpFo5ug843VD6GPvfB0Kdh03cw+2H7VI5xloKknIZRtq8NjzHX/aurFkNSnLleWCkHYNAkyM00lZnKUlAlJ7QHtLykcnGEx4BP8LmWaiIiIiIiIiIiIhchJeWIiIiIiNRBHUL92fLccMb3DC8cmxmXVGzeGwvinRmWlMRigX4PwoQf4dRh+GAI7PjF9n22zYKA5tCkY9XiufwNc51+m0nOsEXcNHDzgvZXVi2GgY/BgMdg/Wfwy6Tak5iTEg/uvqYtma0atQMPP9i/qmoxJJ9NymnSqfizBq1MW7E1U+Fk8Z8HhTZMg7T9MPgflauSA+DqBm1GQPyvkJdTuT1ERERERERERERqOSXliIiIiIjUUb6ebky5uhMJU0YXGX9qdLvC128s2EnsvhPODk1K0nII3P0HBDaHr6+HhZMrnsyQmQZ7/jBVciqbRFEgMALGvQ1J62H+MxVfl5sFm6ZD2zHgHVC1GACGPgW974NV78HC56u+nzOkxJvEF5dK/FXbxRXCetinUk5wK/CqX/LzQf9nKiotfqXk57nZplJSaHdoVckqOQXajoasNEhYWrV9REREREREREREaikl5YiIiIiIXAR8PVwLX1/fM5ypt/QovL/yneVETJrDp8v2Vkdocr7A5nDHb9BlAix5BT4ZCccr8Puycz7kZUO7K+wTR7vLIeZeWPWuqcBTETvmQWYqdLnRPjFYLDD8Reh+Gyx9DRa/bJ99HSllJzRoU/n14TFwZAtknqz8Hslxpg1ZaQKbQ7e/wPrP4URi8ecbvoa0fVWrklMgcgi4eauFlYiIiIiIiIiIXLSUlCMiIiIichHY/NxwYp++jCX/NwQ/L3e6hAcQ4u9VZM6zs7Zy31fr+GjJHtLOqN1MtXH3NpVqrp4KR+PhvQGw8buy12z7Geo1hrCe9ovjsuchpBvMvL9iiUFx00zbpsjB9ovBYoHRr0Gn8bDoBVjxtv32trfsDNPyqUFU5fcI72Wq2BxcV7n1p47CyYPQtEvZ8wY8BhYXWPyfouN5OSYZLKQbtLq0cjGcz8PHVNvZMbf2tCATERERERERERGxIyXliIiIiIhcBCwWC4G+HoQH+QAQXM+T5f+4hL8ObVVk3txNh3hhzjbGvqV2M9Wu4zVw71Jo3B5+vAt+vLvkCio5Z0ylnLZjKtc2qTRuHnDtJ2ABvr/NtKcqTfph2LUAOo83bZjsycUFxr4N0WPh1ydg7cf23d9eUnaaa4PWld8jrAdgqXwLq+Q4cw0pJynHPxR63A5xX8Ox3efGN3wNqftg8KSqV8kp0Ha0SRQqiE1EREREREREROQioqQcEREREZGL2KPDovjt4YE8MaptkfGEY6fJzMkD4KMle4iYNIdvVu+rjhAvbgHN4NY5MPgJ2PQ9vNcf9q8pOmf3Isg5bVpO2VtgBIx9B5JiYf4zpc/b9B1Y8+zXuupCrm5w1UfQejjMfsQkk9Q0hUk5VWhf5eUPjaJh/6rKrU86m/jSpFP5c/s/DG6e8McUc5+XY1qEhXSF1sMqd35JWg83VXnUwkpERERERERERC5CSsoREREREbnItWnsx90DW/LFHb2KjB8+mcnSnSm8MGcbAD9vSKqO8MTVDQb/HW6bB1jh4+Hw58uQb5Km2DYLvAIgor9jzm83BnrfB6veg60/F39utZrWVWE9q1YlpjxuHnDd5xA5CH66D7bMcNxZlZESb5JPgiKrtk94LziwBvLzbV+bFAvBrcCrfvlz/RpDr7tg03Q4sh02fGOq5AyyY5UcAN9gaNZXSTkiIiIiIiIiInJRUlKOiIiIiIgAMKB1QxKmjOa9Cd0AGPTyH0yYeq5iR3igT+HrlFNZPPxtHOmZOU6P86LVLAYmLoX2V8LvL8CnY+D4HtgxF6JGgau7486+9DkI6QY/PQDH9xZ9lhwHR7Y6rkrO+dy9YPw0CI+BH+6EfSvtt3f6Ifj+dsg4Vrn1KfEQ0NzEWBXhMZB1Eo5ut31tchw0Lad11fn6/Q086sGiyaZKTtMu0Ga47eeWp+1o8z1yfqssERERERERERGRi4CSckREREREpIiuzQKLjUU3rU9S2pnC+x4vLGBG7EE6PvsbWbl5zgzv4ublD1d/BFe+D4c2wtu9ITPNMa2rzufmAdd+Ahbg+9sgN+vcs7hp4OoJ7a9ybAwFPHzhxu/A0w/WTLXfvuu/gM0/wObvK7c+ZSc0jKp6HOFnK1bZ2sLq1FE4eRBCbEjK8QmC3vfC9tmQmgiD7Vwlp0DbUea6Y6799xYREREREREREanBlJQjIiIiIiJFNK7vxaZnhxXe3zMoks7hAcTuSyU7N58ZsQeKzN95+JSzQ7y4WSzQeTxMXAJNOoBvQ2g5xPHnBkbA2HdMi6T5z5ix3CzT/qjdGPAOcHwMBbzqm+orO+YVTRCqiq0zzXXbLNvX5ufBsV32ad8VFAk+DWD/atvWJceZqy2VcgD63G+SvZp2hjYjbFtbUYER0LgjbFdSjoiIiIiIiIiIXFzcqjsAERERERGpefy8zrVCGt6+CYvjj3IqK5c2T80rNndPSgYdQv2dGZ6ASd64YwHkngF3b+ec2W4M9L4fVr4NzfuasTMnnNO66kLRV0Lsl7B7EUSNrNpeKbvg8GbwC4HEZaaFlW9wxdenJkJeFjRoU7U4wCRdhcfYXiknqSApp7Nt67wD4PZfwbO+Y6rkFGg7yrTIOnUU6jV03DkiIiIiIiIiIiI1iCrliIiIiIhIiX5/bDB39G9B1/AA+kQWT1BY+OggAB78OtbZoUkBFxfTzsmZLn0WQrvDTw/Asv+CX1OIdEKlngtFDgKvANgys+p7bZ1hrqNfBWs+xBdPPitTyk5ztUdSDpgWVsd3Q0ZKxdckx0FwK1NFyFaN2oF/qO3rbNF29Nmv7S+OPUdERERERERERKQGUVKOiIiIiIiUqEUDX54eE43FYiEmMpjtk8+1tplxX19aNqxXeB8xaQ6H0jKLrE/PzOFg6hmnxStO4uYB13xiqqocXAedrgcXV+fH4ep+toXV3Kq3sNryk6lOEzUS/MNh22zb1qfEm6vdknJizNWWFlZJcba3rnKmJp3M13b7nOqORERERERERERExGmUlCMiIiIiIhXi5e7KpJFtaR7sQ9dmgQB0Dg8ofP7GApOY8O+523j3j91c8+4K+k1ZxIETp6slXnGgwOZw5QcQ1BK631J9cUSPg6yTsOePyu9xbDcc3mT2slig7RjTEisrveJ7HN0BPg3AJ6jycZwvpAu4uFe8hVVGCpw8YNbVVBaLSaLa8ztkZ1R3NCIiIiIiIiIiIk6hpBwREREREamwiYNa8ufj51oVDYtuXPg6IzuPvHwr7y/ew0u/bGfHYZPU0P+l350epzhB1Ah4cD0ERVZfDJGDwcu/ai2stp5dG32Fuba7HPKyYNeCiu+RstN+VXIA3L2haeeKV8pJijPXmlwpB0xSTm6mSXoSERERERERERG5CCgpR0REREREKm3ioJb8/thgfD1c2Z58kkXbj5Q4LzMnz8mRyUXBzQOiRsOOOZCbXbk9tsyEsJ7gH2bum/U2VW+2zar4Hinx0KB15c4vTXgMJK2v2PtKijXXpp3sG4O9NesLXgFqYSUiIiIiIiIiIhcNJeWIiIiIiEilubpYaNHAl79e0pqdR05x1+drS5y3au9xJ0cmF43osZCZBnv/tH3t8T1waKNpXVXAxRXajoL43yA3q/w9Mo7BmePQMMr288sS3stUlTm0qfy5yXGmlZiXv31jsDdXN2gzAnbMg7zc6o5GRERERERERETE4ZSUIyIiIiIiVWYpZfzfV3UE4JaPV3PFW0vL3ScjK5e1CceZvTHJjtFJndZyCHjWr1wLq4I10WOLjre9HLLTYU8FEn1SdpirPdtXgamUA7B/Vflzk+IgpIa3rirQdjRkpsK+5dUdiYiIiIiIiIiIiMMpKUdERERERKpsQOuGha/7tQpm++QRTLsrhht6NSsc33ggjfTMnBLXn87OJSs3j/b//JVr3lvBA9NisVqtDo9b6gA3T4gaBdtnQ17J31+l2joTQntAQHjR8chB4OEH2yvQwiol3lzt3b6qflPwb1Z+Uk5GCpw8ACFd7Xu+o7S6BNy8bG9hdXgLfD4OVr4L2RmOiU1ERERERERERMTOlJQjIiIiIiJVFh1Sn6m39ODKrqG8Ob4rXu6u9G3ZAIBXru1cOO+TZQkkpGSw60g6Ow6lA5CTl0/0M7/yl6mri+zZ4h9zWa22V1IR7ceZ6isVqWxT4PheSN5g1l7IzRPaDIPtcyE/r+x9UnaaJBP/8LLnVUZ4L5OUU1aCWlKcuTatJZVyPHwhcohJyqlo4t2hTfDZ5bBvBfwyCV5vD7+/aFqHiYiIiIiIiIiI1GBKyhEREREREbu4pF1jXr++C8H1PIuMX9M9jG/v7g3Aa/PjGfzKH1z62mKGv7EYgK9WJgKUmIBz3fsrHBy11AmRQ0xlm602tLAqmNvuipKft7scTqfAvpVl75MSD8GtwMW14mdXVHgMpCdD2oHS5yTHmmvTTvY/31Hajoa0/SbZpjzJG01Cjqsn3Lscbv8NmvWBP18yyTlzH4cTiY6PWUREREREREREpBKUlCMiIiIiIg7XprFfiePHM7J5dtbWImN/Pj64yP2+Y6fL3Htm7EE6Pvur2l1dzNy9IGqkbS2stsyEkG4Q2Lzk560uM4kg28ppYZUSDw3a2BZvRYX3MteyWlglxUFQS/Dyd0wMjtBmBGApv4VVUpxJyHH3hdvmQHBLaBYDN3wN962CDlfB2k/gza7ww11waLNTwq/18nLh27+YPwMiIiIiIiIiIuJQSsoRERERERGHC/T1YO+/R/HN2Yo5BV7+dUexuQ3qefKvKzsU3g98+XfGvr2sxH2tVit/+zaO9MxcFmw7Yt+gpXZpPw7OnIC9i8ufeyIBkuNKbl1VwLMetBxqEn1KS/jKOWOqtDgqKadxB3D3gf2rS5+TvAFCaknrqgL1GkKz3mUn5RxcD59fAZ5+cOtsCIos+rxRWxj3Djy0AXrfCzvmwnv94MtrIGFpxVtjXYw2fw/bfoZFkyE/v7qjERERERERERGp05SUIyIiIiIiTmGxWOjePJCYFkGFY1+v3ldsnq+nG1d2DS0ytmF/KusSTxSb2+Ifcwtf3/X5WjtGK7VOy0vAox5s/an8uQVzoseWPa/dGNNmKXlDyc+P7Qas0KC1TaFWmKsbhHYvvVJORoqJr2ktS8oB08Lq8CaTIHWhA+vg83Gm+s+tcyCoRen7+IfC8H/Bw5th6FOQFAufjob/dYdZf4PNP8ApJewVyss1rb/cfeHYLtizqLojEhERERERERGp05SUIyIiIiIiTuPu6sK39/Rh94ujiozP/mv/Ivc+Hm7seGEEq5+8pHDs6ZmmNc1bi3by4eI9XPfeimL7HzuVBcCahOOs3HPM3uFLTebuZdoibZ9tEg/KsmUmhHSFwIiy57UZCRbX0ltYpcSbq6Mq5QCEx8ChTZCdUfxZUpy51rZKOQBRZ38GbJ9bdHz/GvhiHHgHmISc0tqLXcg7EAY+bpJzxrwBwa1g0/fw/e3wSmt4OwbmPGYSsjIu4p8Nm6bD8T0w7m3wbQSr3q/uiERERERERERE6jQl5YiIiIiIiNO5ulh496ZuhfftQ+rzx2ODWfXEuSQcTzdXGvl5Fbay2pp8krx8K6/8Fs+/5m5jdcLxYvu+9+duAK59bwXjP1jJusTic6QOaz8OTh+DhCWlzzmRCEnrIbqM1lUFfIOheV+T6FOSlJ2AxSSAOEp4DFjzTDunCyXHmmvTzo4731GCW0KjaNN2qsC+VfDFleATDLfNhYBmtu/r7g09boObvoO/J8Cdi+DS58A/DOKmwXc3w8uR8E5fmPd32Lmg5re6mvUQzHm06vvk5cLi/0CTTub7v8ftsPO3sxWfRERERERERETEEZSUIyIiIiIi1aJDqH/ha4vFQkQDXxrX9yo276aYc5Uylu9OKfb85Ws6sfX54QBk5+bz/p/nPmC++l1TTedoehZrE46TnZvPzsPpHDmZabf3ITVIq0vPtrCaWfqcirauKtDuCji6/WwCzgVSdkBAOHj42B5rRYX1MNeSWlglxUFQpGnzVBtFjYLEZXD6OCSugC+vgnqNTIUc/7Cq7+/qBmHdof/fYMIPMCkR7pgPQ5+Geg1h3Wfw1dUw/VY4k1r18xwh4xis/wLWfAS7FlRtr43fmio5g/8BFotJXnJxh9Uf2CdWEREREREREREpRkk5IiIiIiJSLcKDfLhrQAv+O7781jvtmtYH4C9TVxeO9YwIBGBQVEN8PNzoGRHIZysS+fe87YVzGvp5kpmTR89/LeCa91bQ5ql5XPb6Ynq9uJDjGdl2fkdS7dy9oc1w2FZGC6utM01lmaAWFduz7WhzLamFVUq8Y1tXAfgEQYMo2L+6+LPkDaYNV23VdjRY82Hh8/Dl1eDX5GxCTqhjznN1h/BeMPAxuPknk6Rz6bOmEud8mF8AACAASURBVNJ7/WHfSsecWxXbZ5lKSb4NTbWcnDOV2ycvFxa/bL73o0aaMb8mprpU7FeQlW6/mEVEREREREREpJCSckREREREpNo8OTqasV3K/wD+h3v7FLn/7/guTJ/Yl4Qpo2nkZ6rrRAT7FpnTJzKYo+lZXP6/pSXuufFADa2MIVUTPRZOp5gKLBdK3QcH11WsdVUB/1AI6VY8KSc/H1J2OT4pB0wiyYHV5swCGccgbT80LT+prcYK6Qp+IbDuE/N1vnUO1G/qvPPdPKH/w3D7b+DiCp+MhD9egvw858VQns0/QlBLuHoqnEiAxa9Ubp+N38CJveeq5BSImQjZ6RD3tV3CFRERERERERGRopSUIyIiIiIiNZ6Phxu39o0ovB8W3aTYnPM/Z/741h60DzHVdXYeOVXinmsSjhe+3puSQe8XF3I0Pcs+AUv1aXUZuPuU3MKqoHVVexuScgDaXQ5J6yHtwLmxkwcg9ww0aF35WCsqPAbOnIBju86NJceaa0gtTsqxWCDmbgjvDbfMNpVbqkNYd7hnCXS8Fv54ET4dA6n7qyeW8506CglLoMNVEDkIOo2HZf+Fozts2ycvB/78j0mCajOi6LOwHhDaHVa/XzTpS0RERERERERE7EJJOSIiIiIiUiv834gohrZtxNd39cbbw7XY8ylXdeLJUe34/bHBDG3bmMeGRxV5/uKVHYvcr0k4AcDOw+kMeeUPDp3MpOe/FjjuDYhzePicbWE1q3jFk60/QZNOEBRp257tLjfX7XPOjR2NN9cGUcXn21t4jLnuX3VuLCnOXJt2dvz5jtT/YbjjV/BrXL1xeNWHqz6AK9+HQxvhvX7nkriqy7afTHuv9lea+2EvgIcvzH4ErNaK77PhG0hNLF4lp0DMRJPwtWeRfeIWEREREREREZFCSsoREREREZFawcfDjY9v7UmflsElPndxsXDXwEhaNDBtrLzciybu3BjTjIQpo5n1QH+u6hbK6r3Huebd5Vz2+uIKnZ+fb+VUVm7V3oQ4R/Q4yDgKicvPjaUdgANrTHsrWzVoDQ3bFm1hlVKQlOOE9lXBrcA7sGhSTnKcSS7y8nf8+ReTzuPhnsWmZdR3N8OshyD7dPXEsmWmSfpqFG3u6zWEy56DxKWwoYLtpvJyYPHLpgVb62Elz4keB76NYNX79olbREREREREREQKKSlHRERERETqrH9fZarjPH5e1ZyOYf60buQHwNrEE8XWpJ3OKXGvj5ftpcM/f+WR7+IcEKnYVevLwM27aAurwtZVV1Zuz7ZjIHEZZBwz9ynx4BUAvg2qFmtFuLhAWC/Yv/rcWFIcNK3FratqsuCWcPuv0O9vsO5T+GAwHNrk3BjSD0HCUvP9en51m643m8pJvz0Fp4+Xvr5A3LSyq+QAuHlAj9th529wbLd94hcREREREREREUBJOSIiIiIiUofd0MtUx7l/SKsi47f1iyh1zf3T1pc4/sKcbQD8uP4gM2IPYLWlfYw4l4cvtBkGW38+18Jqy0xo3NEkXFRGu8tNK6H4eeY+ZaepklNaooO9hfeClB0mESPjGKTthxAl5TiMm4epSvOXmZCZCh9eAjud2N5u68+AtXgSmYsLjHkdMtNg/tNl75GbDUtegdDuJlGtLD1uAxd3WP1BlcIWEREREREREZGilJQjIiIiIiIXHS93V764oxeXtmvExmeH8diwNjx7uWkRs3RXSolr/L3dC18//O0Gvlu73ymxSiVFj4OMI7Bv5dnWVauhfSVaVxVo2hn8m8G22eY+JR4aOqF1VYHwGHM9sBaSY8/GpKQch2s5BO5dDvWbwpJXnXfulh9N26pGbYs/a9we+twPsV8WbdF2oQ3TIHVf2VVyCvg1gfbjIPYryEqvWuwiIiIiIiIiIlJISTkiIiIiInJRGtC6IR/d0pP6Xu48MLQ1t/SNwN3VfHB9MPVM4bzMnDzu/GwtaWdyGN8zvHD87z9sYt6mZKfHLRXUeti5FlZbfzZj0ZVsXQUmqaHdGNi9yCT5ZBwxlXKcJbQbWFxh/yrTugpMopA4nm8D6H4r7FsOKbscf97JJNi3AtpfVfqcQX83SWKzHzYVcS6Umw2LX4HQHtDq0oqdGzMRstMh7uvKxS0iIiIiIiIiIsUoKUdERERERASwWCz0jgwG4PZP1pCXb+VERjZrE06wYNthAGIig/jk1p6Faz5YsqdaYpUK8KwHrS81CTlbfoTGHaBBq/LXlaXtGMjLghXvmHtnJuV4+EKTjiYpJzkOgiLBO8B551/sOt9gkqLivnT8WVtmmuuFravO5+ELo16Go9thxVvFn8d9ZVqcVaRKToGwHqbV1er3IT/f9rhFRERERERERKQYJeWIiIiIiIic9diwKAAyc/No+cRcuk6ez4Spqwqf92/VkCFtG9HnbPLOtuSTAOTlW0k7k8P8rYeJmDSHDftTnR+8FBc9Dk4dggNrILoKrasKNOsNPg1g3Sfm3plJOWBaWB1cBwfXq3WVs/k1MdWX4r6GvFzHnrVlhknAKi+JLGoEtLsc/vwPnEg4N56bbVpthfWEVpfYdnbMRDi2C/YssjlsEREREREREREpTkk5IiIiIiIiZ3UOD2BA6wYkHjtd7Nnef4+ioZ8nANf1DAMgM8dUk5g8eyudn/uNh781bYXGvr3MSRFLmdqMADcv8zp6XNX3c3GFtqMg5zS4ekBA86rvaYvwXubskwchREk5Ttd1gkny2r3QcWek7ocDq8uuknO+ES+Z78s5j4HVasZiv7C9Sk6B6HHg2whWvW/bOhERERERERERKZGSckRERERERM4T4u9dbGzeQwOwnPfh9pVdw7h/SEtcLJCZk8enyxMAOJV1roLG1e8ud3isUg7PeqZCTmgPaGinqjbtrjDXoJbg6mafPSsqPObca1XKcb42w8G3oUl6cZStFWhddT7/UBjyJOyaD1t/gtwsWPIahPWClkNtP9/NA3rcDjt/g2O7bV8vIiIiIiIiIiJFOPn/IIqIiIiIiNRsBdVwABKmjC51XuewAPKt0PbpX0p8vi7xBPn5VlxcbKxUIfY19m2w5ttvvxYDwbM+NIyy354V5R8GfiGQngRNOzv//Iudqzt0uh5WvQenjkK9hvY/Y/OPJuEqKLLia3rdDRu+hl8mwYm9cPIAjP2f7VVyCvS4zbS/Wv0BjHypcnuIiIiIiIiIiAigSjkiIiIiIiJFdI8IBGBYdOMy53VrHljk3t21+AfgsftTS10ftz+V9MycSkQoNnF1BzfP8udVlJsnTPgRLn3WfntWlMUCkYOhcQfwDnD++WJaWOXnwsZv7b/3iQRIWl/xKjkFXN1gzBuQfggWPAvhvSFySOXj8GsC7cdB7FeQlV75fURERERERERERJVyREREREREzjckqhGrn7yE+l7uZc5rUM+TS9s1IuHYabzdXfn5gX58vXo/w9s35rHpG/h9x1GmLt1D9+bdC9f886fNfLYiscg+0yf2oWdEEJe8+gdBvh5Mn9jXIe9L7Ci8Z/WdPfoV06JIqkejdqYdWuwX0Of+ylejKcmWGeZqa1IOQFh36HknrPkQBk+qelwxE2HTdIj7GmLurtpeYuRmw64F0GYEuOjfyImIiIiIiIhcLCxWq7W6YyjUo0cP69q1a6s7DBERERERkSpJz8yh47O/ATDtrhjWJZzg1fnxJc4NDfBmzoP96fL8fAA2PTsMv3ISgkSkGq37FGY9BHcuMskw9vL+QHBxg7sWVW59Xg4c3gwhXe0Tz4dDITMN7l+jJBJ7iP0SfrofRr6sRCcRERERERGROsBisayzWq09ypun/6siIiIiIiJiZ/U8zxUlvfHDVaUm5AB0DPVnx6FzLWI6Pvsbp7JyyczJY13iCYfGKSKV0P4qcPM21XLs5dhuSN5g9q4sV3f7JeSAqZZzbBfsqWSSUGlq0D8Oc6qEpea68DlI3V+9sYiIiIiIiIiI0ygpR0RERERExM4sFgvvTehW4rPJY9tzz8BI4l8YycgOTVix5xhbk08WmdPhn7/S9ulfuPrd5dz31TpnhCwiFeVVH9qPg80/QPZp++xZ2LpqnH32s4foceDbCBZOhnWfQcIySD9kW1JNbrZJNlr/Bcx9HKYOh3+HwYeXQOo+x8VeEyUsg7Be5us3++GLNzlJRERERERE5CLjVv4UERERERERsdXgqEYljv+lT0Thay93V9LO5PDcrK1EBPuQcKz4B/xzNx0iISWDiAa+AJzJzuN0di7B9TwdEreIVEDXCbDha9j2M3QeX/X9tsyA8BjwD6v6Xvbi5gFDn4K5j8GsB8+Ne9SD4JYQ1BKCW5nXwa1M7Mf3wqGNkLwRDm2AI9shP+fcusYdoOM1sHkGvD8Irv0UIgdVy9tzqtR9kLYP+j4AHa6CXybBpu+h07XVHZmIiIiIiIiIOJjFWoP+ZU6PHj2sa9eure4wRERERERE7CL1dDZvLtzFx8v2ApAwZXSR55+vSOCZn7YAMKZTUyaNbEv/l34vca89L47CxcXCHZ+uYeH2I3xya08GRzXEYrE49D2ISAmsVnizq0lEuXV21fY6Gg9v94QRU6D3vfaJz57y8yBtv2lldWyPuR7fba6p+8CaX3yNTwNo2gmadDp77QxBkeBytmDzsd3wzY2QEg+XTYY+94MzfpZt+AZWvQedxptkKu8Ax58JsOFbmHE3TFwKjaJh6jA4sRfuXwO+wc6JQURERERERETsymKxrLNarT3Km6dKOSIiIiIiIg4S4OPBM5dHs+lgKuO6hhZ7PqZTSGFSztXdwwgL9OGtG7vSpL4XAF3CA2j15DwAPlmewB39W7Bw+xEAbvt0Df++qiM39GrmpHcjIoUsFlMtZ9FkOL7HJJxU1pYZgAWix9otPLtycYXACPOr1QXPcrPhRIJJ0kndD4HNTSKOX5Oyk2yCW8KdC2DmffDbk5AUC1f8Dzx8HPc+rFb48z9w8iD88ndY+Jyp2tPjDgjp4rhzARKXgpe/SchxcTXv9f2BpmLO1R869mwRERERERERqVaqlCMiIiIiIlKNnpu1hd6RwQxv36TE5498F8eP6w+W+KxjqD8z7++Hq4uq5Yg4XdpBeKMDDHjUtHmqrLd7g3cg3D7PfrHVFlYrLH0NFk42ra3Gf2mSfxwhYRl8OgrGvWuSY9ZOhY3TIfcMhPaAnndC+yvB3cv+Z/+vOwS3hhu/OTf2+4vw50tw0/fQ+jL7nykiIiIiIiIiDlXRSjlKyhEREREREanher+4kEMnM0t8FuDjjq+HGwdTzxSO7XhhBJ5urgAcPpmJp5sLqadz8Pd2J9DXwykxi1wUvrwGjmyFv20yFVBsdWQbvNMbRr0Cve6yf3y1xc4F8MPtYHGBaz6GlkPtf8aP98COufDodvDwNWNnUk1LqzUfwbGdJjmq6wTocXvVqh+dL/0QvBpl2nT1e/DceG4WvDcAck7DfSvA088+54mIiIiIiIiIU1Q0KcfFGcGIiIiIiIhI5T0xul3h6/Yh9Xnzhq6F96mnc4ok5AA8Nn0juXn5AMS8uJAuz89n8Ct/0GfKQucELHKx6DrBtEPa/Xvl1m+ZYRJR2l1h37hqm9aXwt1/gF8IfHk1LH3DVNGxlzOpsHWmaVdVkJAD4B0AvSfCA2vg5p+hxUBY8Q682dXEcepI1c9OXG6uEf2Kjrt5mjZWaQdMpSARERERERERqZOUlCMiIiIiIlLDBXi7F76e8+AArugcws8P9Ct1/qwNSbR6ch5j31paZDwzJ99hMYpclKJGgncQxH5h+1qrFTb/CM37gV9j+8dW2wRFwp3zIXosLPgnfH8bZJ2yz96bpkNuJnS7ueTnFgtEDoLrPoeHN8Pgf8DuRbD6w6qfnbgM3H2hSefiz5rFmApJqz+A/aurfpaIiIiIiIiI1DhKyhEREREREanherUIYlh0Y+Y/PLBwrFNYAJueHVZ4//r1nUmYMrrIug0H0ort1W/KIto8NY+s3Lxizw6mnuFERrYdIxep49w8ofN42D4HMo7ZtvbwFtMyqf2VjomtNvLwhWs+gcueh60/wdRhkHmy6vuu/xyadISmXcqfWz8EBk+CZn1Mu6uqSlxukm9c3Up+fskzUD8Ufv6raWklIiIiIiIiInWKknJERERERERqOC93Vz64uQetG/sVGffzcmfHCyPY++9RXNk1DICZ9/cjyNejyLwJvZthsZjXB1PPkJ2bz89xScyIPUBS6hmsVitZuXn0m7KIrpPns//4aae8L5E6oesEyM8x1VhsseVH07oqeqxj4qqtLBbo9xDc8A0c2QKr3qvafklxcGgjdLuFwh+EFRE1Eg5vhhMJlT874xgc2WqqIZXG0w8ufwOOboclr1X+LBERERERERGpkZSUIyIiIiIiUot5urliOe+D5i7hAaz8xyVF5tzSJ4J1T11WZOzx7zfy8Lcb6DtlEU/O3MwP6w4WPhv48u/k51sdG7hIXdG4PYR0NS2srBX8c2O1wpYZ0GIg+DZwbHy1VZvhEDUalr8FZ05Ufp/1n4GbF3S81rZ1UaPMdccvlT973wpzLSspB6D1ZSa+Ja/CkW2VP09EREREREREahwl5YiIiIiIiNQxHm4uTJ/YhzkP9ufBoa1o1aheseo555u2ah9PzNhUeG+1mqSdL1Yk2CWet3/fRcSkOVz3/gq77CdS43SdYKqqJMeVPS8vF7bNgk/HwPE90OFq58RXWw35B2SlwYp3Krc+OwM2fQ/R48A7wLa1wS2hYVvYMadyZ4NpXeXmBaHdyp87YoqpmvPzXyG/eHtBEREREREREamdlJQjIiIiIiJSB/WMCKJ9iD+PDIsqrKSz+olL+On+frxybedy1/+w/gBP/7SFz1ckVDmWl3/dYc7fe5xWT8xlxe5jVd5TpEbpcI1Jvoj9suTnp4/Dsv/Cm13h2wmQmgiXTYYuNzk3ztqmSUfT3mvlu+ZraKutP0HWSeh2c+XOjxoJCcsqX6kncSmE9QQ3z/Ln+jaAkS/BgTWw+sPS5+Vmw/G9sHcJbPgG4n+tXGwiIiIiIiIi4hRKyhEREREREblINKrvRefwAK7pHlY4tvDRQYWvh0Q15Jkx0UXWPPPTFq54a2mlz9ybklHkPjffynOztlR6P5EayTsA2l0Bm6ZDzplz44e3wqyH4LVomP8MBDaH67+EB+Og34Pg4lp9MdcWgyZB9ilY/j/b167/HIJaQvO+lTs7ajRY82DnAtvXZqbBoU3lt646X8drodVlsPB52PCtqRD0yxPw3c3w4VB4pQ280Aje7AKfjYEZ98C06yH9sO3xiYiIiIiIiIhTuFV3ACIiIiIiIuJ8397dmxOnc2jZsB6jOzZlzqZkNh1M452buvP87K0AXNU1lB9jD7LxQFqlz/lyZWKxse2H0vm/7zcQ6OOBp5sLjwyLqvT+IjVG1wmw6TvY+rNpQ7TqXdi72FTQ6XQd9LoHmnSo7ihrn8bR0OEqWPU+9LnfVJSpiKPxsG8FXPocnK0WZrPQ7uDbyLSw6nStbWv3rQJrvm0JQRYLjHkN3ukDM+42Y+4+UD8U/MOg9TBzLfiVmwXTroP4edD9VtviExERERERERGnUFKOiIiIiIjIRSgmMrjw9VNj2rH76Ck+vLkH3h6u3NKnOU0DvLlrQCQ/xh4EICcvH3dX24qtztqQxNSlewFY9Ogghr76Z+Gz79YeKHytpBypEyIGQEAzmDnRJGPUD4VL/mmSJXyCqju62m3QJNgyA5a9AcNeqNia2M/BxQ0631D5c11cIGoEbJ5h2ka5eVR8beIycHE37atsEdAMJi41bbf8w8E7sPSkIqvVzN8+V0k5IiIiIiIiIjWU2leJiIiIiIhc5Jr6e/PL3wYSHuQDwHNjOzBxUEtcXSw8MaotALH7Uiu8n9VqZd6mZP76dWzhWHiQDzPu68v3E/swLLpxkfl7UzI4kZFth3ciUo1cXGDwExA5BK79DB7aCAMeUUKOPTRsAx2vg9UfVaxVU242xH0NbUaAX+Py55clajRkp0PCEtvWJS6D0G7g4WP7mUEtoGln871TVpUfi8XEt+cPyDpl+zkiIiIiIiIi4nBKyhEREREREZFSuZz9QPi691eQnplT7vyMrFxa/GMu9361vsi4u6sLXZsF0iMiiNev70KT+l6Fz4a88gddJ88n8VgGT8/czKPfbSDtTPlnidQ4XW6Av/wI7ceBq4oT29Wg/4O8bFMtpzzx8+B0CnS7pernRg4yLaR2zK34muwMSIq1rXVVZbUdBXlZsOd3x58lIiIiIiIiIjZTUo6IiIiIiIiUakLv5oWvV+89zv7jp0ucZ7VaSUo9w4b9RSvq+Hi4sunZYUXGfD3dWPnEJWx7fkSR8UEv/8EXKxP5Yf0BOj/3GycrkAQkIheJ4JamFdWaqXAyuey56z837cNaXVL1c929oeVQ2DHPtIuqiANrID8Xmvev+vnladYHvPxNCysRERERERERqXGUlCMiIiIiIiKl8nJ35as7YwC447O1DPjP76SeziYzJ4+MrFzWJR7n1NnqOH2nLOLGj1YVWT//kUH4ebmXuLe3h2uZZ8cfSi9yf371nDZPziNi0pzKvCURqa0GPQ7WPFjyaulzUvfDroXQdQK4lP0zpsKiRsHJg5C8oWLzE5aBxQXCe9nn/LK4ukPr4RD/C+TlOv48EREREREREbGJaimLiIiIiIhImZoF+RS57/L8/Aqtiwj2ITTAu8w52yePwMViYdqqRJ6dtRWAvi2DWb77GLuOnKJHRBAAv+84wm2frGHyuA78vv0I2Xn5ADw5YxNPjGqHr6f+eitS5wVGmGSb9Z9Bv4cgILz4nNgvzbXLTfY7t81wk2SzYy6EdCl/fuJyaNoZvOrbL4aytB0Fm76D/asgop9zzhQRERERERGRClGlHBERERERESlTeJAPDwxpVeH5D1/ahrVPXcqcBweUO9fL3RUPNxdu7deCzc8NZ/mkoXx6m6kucehkZuG8dQknAHh65mYWbT9SOP7Vqn20/+ev5J5N0hGROm7AY6aNVEnVcvLzTFJOyyEQ2Lz488rybQDhMSYppzw5maZ9VXMnJse0uhRcPSoWn4iIiIiIiIg4lZJyREREREREpFyPDY8iYcroMucs/fsQBrRuwI0xzWhQz9Pm6jX1PN0ICfDGw838VfWNBTtZvfc4uXn5vPX7rhLnF/jPrztsOktEaqmAcOh+C8R+AScSiz7b/TucPADdbrb/uVGj4NAmSN1X9ryk9ZCXBc372j+G0nj6QYuBsH2OSVgSERERERERkRpDSTkiIiIiIiJis0vbNSpynzBlNGGBPnxxRwwN/Tztds51769g/Acri43/+fhgNj07jP+ON61kDp44Q3pmDk/M2ER6Zo7dzq+q7Nx8jmdkV3cYInVL/0fA4gqLXy46vv4z8Ak2CTT2VrDnjnllz0tYZq7N+tg/hrJEjYITe+HodueeKyIiIiIiIiJlsu2fLYqIiIiIiMhFbfZf+3MmJ4+eEUGkns4m5VQ2ri4Wu5/j7+1O2hmTXLM20bSuemp0O8Z0CmHxzqM0C/LBYrEwtksoU5fuJT0rl8mzt/Ld2gNMW7WP7ZNH4OXuave4Kmry7K1MXbq38H7b8yPw9qi+eETqFP9Q6HEbrP4Q+j8MwS3h1FHTvilmIrjZLzGwUINW0KDN2TPuKX1e4jJo1B58guwfQ1miRsGcR0y1nEbtnHu2iIiIiIiIiJRKlXJERERERESkwjqE+tMzwnzYHODjQatG9WjRwNfu52z457BiYxN6N6eJvxfX9QjHYjmXCBQa4M3BE6fJyz8398PFe+weU0XtPJxeJCEHYP62w9UUjUgd1f8RcPU4Vy1nw9eQnwtd/+K4M6NGQcJSOJNa8vO8HNi/GiL6OS6G0tRvCiHdTNKQiIiIiIiIiNQYSsoRERERERGRGmnqLT0AeOP6LmVWvgkL9Gb30Qx+WH+gcGznkVNOibHA4ZOZ/LrlEEmpZ/gz/mix5w9+HevUeETqPL/G0PMO2PgtpOyE9Z9DeAw0auu4M6NGmcSfXQtKfp68AXIyoHlfx8VQlraj4OA6OJlcPeeLiIiIiIiISDFKyhEREREREZEa6ZJ2jUmYMppxXUPLbEXV1N+7yL2/tzsHU884Orwinpq5mXu+WEffKYt4Yc42AP53Q1d8zmtZlZ2bX9pyzmTn8caCeDJz8hweq0id0e9v4OYF390Cx3ZCt5sde15YD/BtWHo1msRl5tq8GirlAESNNtf4edVzvoiIiIiIiIgUo6QcERERERERqdVaNapX+PqN67vQMdSfdYknnBpDemZOsbHRHZuy9fkR9I407b7u+2pdqes/XraXNxbs5KtV++wal9VqZe6mZE5n59p1X5EaoV5D6HU3HNkCHn7Q/krHnufiCm1GwM75kJtd/HnCMghuDfUaOTaO0jRqB4ERsF0trERERERERERqCiXliIiIiIiISK02sE1DHh8exTXdwxjXNZSlu1IASHJitZwjJ7OKjbm4WAD4vxGmnc6CbUdKXX88w3zAP3n2VrYmnbRbXD/FJXHfV+u5+t0VdttTpEbp9xB4+UPn8eDh6/jzokZB1klIXFp0PD8P9q2svtZVABaLqZaz90/ISq/cHktfh49HwIyJ8MdLsPE72L8GMlLAarVvvCIiIiIiIiIXAbfqDkBERERERESkqu4f0qrw9avXdubR6RtITjtDSIB3GavsY/PBNPakZBTee7q5MKZTSOF9t2aB9IwIZM/RjJKWAzB16d7C16PeXELClNF2ie1v38YBsC35JF+uTGRC7+Z22VekxvAJggfWmsQcZ4gcDG7esGMetBx6bvzwZshKg4j+zomjNG1Hwcq3YddCaD/OtrXJG2Dh86baTuo+2PB10ecefhAUAYEtIKgFNGhjkpR8guwVvYiIiIiIiEido6QcERERERERqVM6hZkP569+dwXbtyL/xAAAIABJREFUJ4/Ay93VoedtSzaVbe4ZFMn7f+7h98cGF0sGGtGhKZNnb+VoehYN/TwdGk8B6wVVLaYu3aukHKmbnNkuysPHJONsnwsj/2Oq0wAkLjfX6qyUAxDeG7wDYcdc25Jy8vNhzqPgHQR3LTJ75GRCaiIc3wPH98KJveZ6ZKtJSsrPAVcPk5jT7S8QOcS0+BIRERERERGRQkrKERERERERkTolLNCn8PW/5mxj8rgOxeZsOpDGusTj3NqvRZHxvHwrr/62g5EdmtIxrHjljQ37U0k5lcUl7RoXjh1KywTg4UvbMGlEWywFH9Kfp10TPwB2HEovkpSTl2/lvwvii83PyMrF17Nqf2Wfvu4AAC0b+tKmsR8r9xzDarWWGJ+I2CBqJOyYA4c2QtPOZixhKQQ0B/+w6o3N1Q3ajDBJM3k54OpesXWxX8CBNTDuPZOQA+DuBQ2jzK8L5efB4S0QNw02fgNbZ0L9MOh6E3S5CQKVACh2lJkGsx+B4f8CvybVHY2IiIiIiIhNXKo7ABERERERERF78vZwZcpVHQH4YmUi7/6xu9icy99ayrOztpKcdqbI+OyNSbzzx24uf2spWbl55OVbyc3L57cthzidncvYt5dxx2drOZ2dW7jm6Kks/L3d8XJ3LTXhpW3T+gBsTkojIyuXl37ZTmZOHvO3HuLNRbsA+PVvA+ne3HwY3v6fvxY5ozLeXLgTgP9n777Dq6qyPo5/b3pIQkIaJKGEntA7UkV6s4KOZezYu44aOyoC6js6Knbs48xYABtFmtKV3gkkIaEkQBqEkJCe948dbohJSLs3BX6f5/E55+yzz97rxgs+cJZrfXZLP3q29OF4Zi5zN8fXaE0RwSS9YDGJLwCFhaZSTqtBdRqWVcfxkHUCDq6r3PyMFFj6ArQcCN2vrdwzDo4Q1A3GzYTH9sLkzyCgA6x4Dd7qDl9eDju+N9V2RGrqwFrY+T3s+bmuIxEREREREakyVcoRERERERGR8861/VoyZ/NhNsQd59VFkfg0cua6fi0BKCgobuv07m/RTLuiKzMW7OHDlftLrNHx2UXlrr82OoWRnUy1nJSMHPw8XM4Zj6+HC82buLNyXxKZ2Xm8/3sMJ0/n0jbA0zqnfaAnozs1ZdOB4wD8su0I1/RtQfyJ0zT1csXJseL/ryblVDZXf7iOmVd14/Bxk3DU0q8RXYJN1Z+5Ww4zqXcdV/IQaeg8A6BFf4icD8MiIGkvnE6F0HqSlNN2ODi6mhZbrYdWPH/ZVMg6CRP+WdyOqyqcXKHLVeafEwdh639hy79hzu3g5gPdroHBj0LjoKqvLQKQbJJMSdhSt3GIiIiIiIhUg5JyRERERERE5LwU5O0OmASXBTuOcPh4Jiv3JTOqU3HrqX//cZD7L2lfKiGnIk/M2c6wnQHM3RxP2wAPmlSQlAMmGWhtTAprY1IA+PrPg9Z7/5nSHwcHC7cPbk1BIby6KJLDxzPp9fISUjNyuGVgKFMv63zO9TcdSGXS+6YyxjUfmqOLk0nkGdjOHz8PF5p6uVXpc4pIOTqOM9Vl0g7DgdVmrNXAuo3pDFdPaDPMtNgaO+PciTaH1sPmL2HgA9C0U8339mkJw56EoY9D7AqTnLPpcziZANd+XfP15cKUUpSUE7+pbuMQERERERGpBrWvEhERERERkfPSbYNbE+rXCIBVUcm8+1sMO+LTeGPJPgBcixJWPlhh2lt5uRb/fyvtAz25rl+LctdOzcixtoKKScrAtxJJOTn5BeXeG9jOHwAnRwfuGdYWgLeXR5OakQPA4l1HK1z/TELO2Ya2D7Cehwc1JjrpVIXriEglhE0wx70LTWsdryBo0rpuYzpb2HhTtebYrvLn5OfBL4+CVzBcHGHb/R0coO0lMPkT6H0LRC+FnAzb7iEXjmTT5pGkvZCdXrexiIiIiIiIVJGSckREREREROS81KOFD78/fgnNGpddHeaTm/sC8PnaOAC+vXsACx8awq4Xx7Dk0YuZcVU3Zt/UBwB/Txc++HsvxnRuWuZaDpXo+PLzA4MZ0MaP1yZ146YBrazjUwZX/CI/IS2LzJy8ijf5C2fH4sDaBXqy/XAa499aVeV1ROQv/NuDXzvTwipuDbQaVL3WT/bSYRxggb0Lyp+zYTYc2wHjZprqOvYSNhHysiB6mf32kPNbShQ0DgEKIWFrXUcjIiIiIiJSJUrKERERERERkfPaLw8OLjV2Zc8Q2gR4lBjz83AhPKgxHmdVzBnaIYDLewTz9ZSLGNsliA9v7MOmZ0eWWu/YyewK4wjydue/d17ENX1bMPXS4lZUj47uUGruz/cXx/zQiPYAvLU0qsx1T+fkExox33r94PB21vOzW14FNnYFYPeRk0QePVlhvCJSgY7jYf/vcOpo/WlddYZXU2jexyQNlSX9KCyfBm1HQPhl9o2l1SBw8yk/lvog9zTs+dlUD5L65fQJyEiCLpPMtVpYiYiIiIhIA6OkHBERERERETmv+Xu6sjZiOKueuISoV8ax6OEhvD65G8E+7lzZM8Q6r0kZLahcnBx469qedGzmZR3z83Rl7r3mBfzDI03CzGe39K1STA4OFp6dEM6NF7WikYtTqftdm3vzxW39eHBEe6YMMZV0nB1L/hH+aFoWUcfSeXVRpHXstkGt6dmqCQBPjg2j6VlVgm68qJW1kMfYf60i7LmF3PHlxipV4Fm6+xgP/ndLpeeLnNfCJgCF5jy0dPJfnes4Ho5shbT40vd+fQbyc2D86/av8OPoBB3Hwb6FkJ9r372qI/c0/Pda+Obv8Me7dR2N/FVKUeuqlhdBk1BI2Fyn4YiIiIiIiFSVknJERERERETkvBfs404L30Y4OzoQ1qwxTkUJLhHjwqxz/pr0ci69WjYhbuYEHh7ZgbiZE8pM6KnIlCFtePmKLuXev7hDAI+O6oCXmzMAs36LLnH/ls/WM+rNldb2WwCPj+nIsA4B/HjfIG7/S1ssLzdnXp3UzXqdlVvAkt3H+PcfBwiNmM9Nn64/Z7zzthxmypcb+WlbAtd99EdlP6bI+at5X2jkb/7xL13xqs6FTTDHfQtLju9fATu/h8GPgF/bWoplImSlQdzq2tmvsnKzTDLO/hWmHdmK1+FUYl1HVT8VFJgKS7UtuahKnF97CO4F8UrKERERERGRhkVJOSIiIiIiInLBCvRyresQKsXf0yT9JKZnWccij6Zbz4d1DGDLc6Nwd3HEYrHQvYUPLk6l/8g/vmsQI8OblhibvsBU2lm5L4m8/IJSzxxNy2LhjiM88s0269i6/SlM+WIDuxNOMubNlby1NIqCgsKafUiRhsbBEYY/Cxc/Yf9qM9Xh3wF820LkguKxvByY/5ipODL44dqLpe1wcHKvXy2s8rLh2xsheilc9jZc9z/IOw3LX67ryOqn9R/BW93h9PHa3TclCiyO5jsb0hvSDilxSkREREREGhQl5YiIiIiIiMgFy2KxEDtjPLEzxtd1KOf01LhwAPq9sowdh9M4nZNf4v5jozpWqlqPp6sTs2/uwxe39ePt63qWup+Ynl1q7JbP1nPP16UrEyzdk8j4t1ex91g6by7dx/b4tMp+HJHzR59bof9ddR1F2SwWCBsPsSsh66QZW/eOSXIY/3/g7F57sbg0gnYjTFJOQenkv1qXlwPf3gxRi2Hiv6DXTeDfHvrfDZu/giPbKl7jQrP1a8jLgmO7a3fflGiTkOPkAiG9zJiq5YiIiIiISAOipBwRERERERG5oFksFiz1scrFWUL9Paznl85azaqoJADeurYHcTMn0LW5d5XWu7hDAJd1D6Z/a98S468uiixxHZN0qkRFnkdGdmD71NF0b+FTas11MSlVikFEakHHCVCQa6rBnDho2jOFXwrtR9V+LGETIT0BErbU/t5ny8+F7281bb3G/59JrDpj6OPQyBcWRkChqn9ZJUfB0e3mPGlPLe8dbRKmAIK6g8UB4jfVbgwiIiIiIiI1oKQcERERERERkXqud6smJa7v/Mq8kAz0cqvRuv2KknLOJNn8uDWB/UmnANiVkMaIf64oMf/BEe1o7OZMxNiwUmst3HmkRrGIiB206AeN/GHvApNoYnGAsTPrJpYOY0wboshf6mZ/KErIuc3EMO416HdHyfvuPqYl2cG1sGte3cRYH+2cA1jAyQ0SIyucbjMFBZAaA37tzLWLBwR2UlKOiIiIiIg0KErKEREREREREWkAZt/Uhwldg0qMdQlpXKM1bxzQip4tfXh9cjfr2KPfmrYtD/ynuJrFu9f34usp/a0VhQa09aORiyMAz03sBMD2w2pfJVLvODhCh7Gw6wfYOx8ufgK8m9dNLI18IXRQ3SXl5OfB3Dtgz08wZnr5bcd63QxNu8CS5yH3dO3GWB8VFpqknNDB0KwrJNViUk7aIdMy60ylHIDgnpCwWZWMRERERESkwVBSjoiIiIiIiEgDMLJTU969oRd3DW0DwJx7BuDl5lyjNQO93Jh37yA6NPXijiGtAdh66AQA+UUvPCf1as6EbkEMaudf4tndL41l6aNDuW1QKKM6NQUgNSOnRvGIiB2EjTctrALC4KJ76ziWSyF5HyTtq919C/Jh3l2m+s2ol2HAfeXPdXA01YTSDsHaWbUXY311dIf5d9blKvMdSqzF9lUpUebod1ZSTkhvOH0cjsfWXhwiIiIiIiI1oKQcERERERERkQbkqfHhxM2cQO9WvjZd9+GRHaznoRHzOZCSyYSuQfzf1d3KfaZdoBcWi4Ub+rcEsLa+EpF6pO1wCL8MLn8PnFzqNpaw8eZYm9VyCvLhh3th5/cwcioMerDiZ1oPgfBLYfUbcDLB3hHWbzvngIMThF8OgeGQmQwZybWzd3K0Ofr/JSkHIH5z7cQgIiIiIiJSQ0rKERERERERERE8XJ14enxYibFOwY2tLavOpaVvIwAOpmZWa++10cmERsxnd8JJ8vILKFRbEhHbcXaHv30FzXvXdSSmdVZwz9pLyikogJ8egO3/g+HPwuBHKv/sqJdNQs/SqXYLr94rLISdc6HNJeDhZyrlQO1Vy0mJAldv8AgoHgsMByc3JeWIiIiIiEiDoaQcEREREREREQHgko6BJa6bN3Gv1HMhRfNe/Hk362NTCY2Yz/3/KX5hujvhJKER87np0/XkFxQn3GTl5vPdxkNcP/tPAMa/vYp2zyzkse+21fSjiEh9FTYR4jfVTgWaJc/B1q9h2FMw9PGqPevb2rS52v4NHNpgn/jqu8MbIO0gdJ1srgPDzTEpsnb2T44Cv7ZwdnKoozMEdTffIRERERERkQZASTkiIiIiIiIiAkD7pl7EzZxAzPTxfHPnRVzWPbhSz7k6OQKQdjqXD1fEAPDL9iMUFCXg3PTpegBW7kviH99ts1bC6fvKUh7/fnup9eZujmfelsM1/jwiUg+FX2qOkfPtu8+hDbDuXehzGwyLqN4aQx4Fz2aw6ElTdedCs+N7U5WmY1HbMa8gU7mm1irlRJdsXXVGSG84sg3y82onDhERERERkRpQUo6IiIiIiIiIlODoYKF/G79Kta46Y2R4UwCWRSZax1IycvhkdSzJp7KtY/O2xPPiz7u5+6tNpGeV/0L1kW9ULUfkvOTfAfza2beFVX4u/PwQNA6GUS9Vfx1XLxj5gqnKsuNb28XXEOTnwa550H40uDU2YxYLBIbVTqWcnAw4GQ9+ZSTlBPeCvNOQVEvJQSIiIiIiIjWgpBwRERERERERqbHbBoeWGvt24yE+XR1rvW4T4AHA52vjWLTraIm5LX0blXq+4KxWVyJynrBYTAuruNVw+rh99lg3CxJ3wfjXTWJNTXS71iSBLJ0K2adsEl6DcGA1ZCRCl0klxwPCTKWcQjv//pxiqq7h3670vZBe5qgWViIiIiIi0gAoKUdEREREREREamxgW/9SY6//upf4E6eZMrg1O18cw6KHhpa4f23fFsTNnMCmZ0ey/LGL+eWBwcTOGM/Ll3cGIGJu6dZWInIeCL8UCvJg32Lbr526H35/1ewRNqHm6zk4wLhXIf0IrH6z5us1FDu+BxdP6DCm5HhgOJxOhYwk++6fEmWOZVXK8W0Dbj4Qv9m+MYiIiIiIiNiAknJERERERERExCY++LupXvDQiJIvUSf1bo6nqxMuTg48OTbMOj5zUjcA/DxdcXJ0oEuINxaLhSHtAwD4duPhWopcRGpVcC/wbAaRP9t23cJC+OVRcHCCca/Zbt0W/aDr1bD2HTh+wHbr1ld5ObDnJ5PU5Oxe8l5A0e/hiXZuHZUcDVjAr23pexaLqZajpBwREREREWkAlJQjIiIiIiIiIjYxtksQ+6aN4+GR7XmtKOEGIKxZcfuYdoGeAIwMDyx3nVB/DyZ0CwJgeeQxO0UrInXGwcEkfEQvg9zTtlt3x3ew/zcY+QI0DrbdugAjp4LFAZY8b9t166OYZZCVBl0ml74XGG6OSZH2jSElCrxblE4KOiOkNyTuhpxM+8YhIiIiIiJSQ0rKERERERERERGbcXFywGKxML5bECPDA1n22MVYLBbr/ZHhgXx71wA+vqnPOdcJ8HQF4LbPN5KTV1DiXnZePtGJ6bYPXkRqT/hEyM2EmN9ss15mKix6CkL6QJ/bbLPm2bybw+BHYPcPELfa9uvXJzvngHsTaDOs9D3PpqZ1lN0r5USBf7vy7wf3gsJ8OKo2hyIiIiIiUr8pKUdEREREREREbM7T1YnZN/elbYBniXGLxUK/1r4lEnXK8sTYjtbzDs8u5NsNh6zXMxZEMvKNlSScsGGFDRGpXa0Gg6s3RP5im/UWPwdZJ+DSt8DB0TZr/tXAB6BxCCx/xT7r1wc5mRC5ADpdDk4upe9bLKZajj0r5RQWQko0+LUvf06IaZdI/Cb7xSEiIiIiImIDSsoRERERERERkXqnkYsTs67vab1+Ys52bv50PaER8/l8bRwAX6yNIzRiPjvj0+ooShGpNicX6DAG9i6E/LyarRW7Crb+2yTNNOtim/jK4tIIBtwHB9dC/Gb77VOetHhTWSgv23577FsEuRnQZVL5cwLCTKWcwkL7xJB+FHJOgf85knK8mkHj5nXz70FERERERKQKlJQjIiIiIiIiIvXSxG7BXNevpfV6xb6kEvc/XLnfzHtnNZk5eXy2JpbUjJxajVFEaiB8IpxOhYPrqr9Gbhb88jA0CYWLn7RZaOXqeSO4eMEf79l/r7/66X746gp4rQ18dwvs+B5On7DtHjvngGczaDWo/DmB4aYq0aljtt37jJQoc/Rre+55IT1VKUdEREREROo9JeWIiIiIiIiISL019bJOXN+/ZYmxp8eH4eFSsj1Np+d/5cWfd/Piz7tqMzwRqYm2I8DRtWYtrFb907Q6mvgmOLvbLrbyuDWGXjfBrnmmck1tOZUE+3+HzldB18kQtwbm3A6vt4Uvr4D1H9c8nqw0iFoMXa46dwuwgDBzTNxTs/3Kk3wmKecclXIAQnrD8VjITLVPHCIiIiIiIjagpBwRERERERERqbdcnRyZfmVXOjT1BGDDMyO5c2hbQpqU/fL9x60JhEbM528frmNVVFKZc0SknnD1hLbDIXJ+9VohJUbC6jeh6zVmndrS/y4oLID1H9Xenrt/MHsOfRwufQse2wu3L4EB90PaIVjwD3izE3w0DFa+bn42VbXnF8jPOXfrKjCVcgCSqrFHZaTEgJM7NA4597yQ3uaYoBZWIiIiIiJSfykpR0RERERERETqvZ/uH8y6p4YT4OUKwH/vuIi/9WnBuqeKX8Rf1MbXev5nbCo3frKe0Ij5LI88Rl5+Qa3HLCKVED7RJJUc2Va15woKTNsqV08YM90+sZWnSSsIvxQ2fQbZp2pnz13zICAcmnYy1w4O0KIfjHoRHtgE922AkVPBwQmWT4P3+sOyl6uW7LRzDvi0Kk52KY9HALj72q9STkoU+LUzn/FcgnoAFohXUo6IiIiIiNRfSsoRERERERERkXrPzdmRIO/i6jh+nq68OrkbQd7uxEwfz95pY0k4kVXms7d9vpF2zyxk79H0Ku+bX1BIVm5+teMWkQp0GAsWh6q3sNr8BRxcB6OngWeAfWI7lwH3m3ZPW/9j/71OJsCBtaatVHkCOsDgR2DKUlNFp+eNsOr/YO4dkJdd8R4ZyaY9VpdJYLGce67FYqrl2KtSTnIU+LereJ5bY/DvAPGb7BOHiIiIiIiIDSgpR0REREREREQaNEcHC65Ojsy4qisA7QI9y5w35l8r+WbDQfILKl854pFvthL23CIKq9NaR0Qq5uEPLQea1kmVlX4MlrwAoUOgxw32i+1cWvSD5n3hz/ehwM6Je7t+AAqh8zmScs7m1QwuewdGvAA7voMvr4DM1HM/s/sHKMyHrpMrt0dAmGmRZevfG/Oy4cQB8GtfufkhvUylHP0eLSIiIiIi9ZSSckRERERERETkvDConT9xMyfw3V0D+PG+Qfz9opY0dnMqMefJOTto+/QC/v3HgQrX23TgOD9tSwDg5V/s1KZFREwLq6Q9kBJz7nkFBaaKyi+PQN5pmPhmxVVd7OmieyF1P+xbZN99ds6BZt0qVz3mDIsFhjwKkz+F+I3wyWgTa3l2zDGJNoGdKrd+YDhkp0H6kcrHVBmpsVBYAP6VTcrpDRmJkHbYtnGIiIiIiIjYiJJyREREREREROS80sTDhe4tfJh2RVc2PTeKXS+OoVljtxJz1sYkn3ONjOw8Jr2/1nr96ZpY8gsKmbflMJk5eXaJW+SC1XG8OZ7dwiovB45sg81fwYLHTVLJjOYwqw/snQ/DIiqfuGEv4ZeBdwtY96799jgeZ5Jqukyq3vNdJsFNP0FmMsweCYc2lJ6TFg8H10KXyZVPcgoIM8dEGycspkSZo18lE5BCepljwmbbxiF169B6+Gd4xYl6IiIiIiINgJJyREREREREROS85ezogIerEzcNbAXA8LBAABbsOHrOllSpGTmlxqbN380j32zjuR92VSuWd3+L5u1lUWTl5vPCjztJy8xVWywRgCatTCWYzV/Cj/fBB0NgejB8OBR+uh+2fA1YoOcNcNksuHs1DHmsrqMGRyfofzccWAMJW+yzx6555tj5yuqv0WoA3L4UXBvDFxNh949/2WOuOXapZHssMJVyAJIiqx9XWZKrmJTTtAs4OEP8JtvGIXXrwBpIT4ClU+s6EhERERGRGnOqeIqIiIiIiIiISMN299C2XNe3JU08XAiNmA9A66cWABD9yjicHB14et4OohNP8cWt/bj/v+YF+8C2fqyNSQFgx+E0AOZsPsw/r+le5Rhe/3UvAG8s2QfAF+tMC60f7htEjxY+VVorNSOH9KxcWvl5VDkOkXqp69Ww5Dk4fdwk6Ay41xyDeoBvG3Cop/9vYa8b4fcZsO49mPSx7dffORea9zWJSzXh3w6mLIX/Xgff3gyjX4YB95vKODvnQHBP8Gtb+fU8/KGRvx0q5USDZzNwa1y5+U6u0KwrxKtSznnlTIWcPT+Z6k4t+tZtPCIiIiIiNVBP/zQrIiIiIiIiImI7Dg4Wmni4ALDwoSEl7h1LzyY9K5f//HmQ9bGpjH97FdsOnQDgun4tWfH4MAA2HjhufSY0Yj7LI49Vev//rj9Y7r0XfipdeSczJ49Zy6M4lV26Vdavu47S6+UlXPz676q0I+ePAffD4zHmn5t+gFEvQdfJJpmkvibkALh5Q6+bTLWZkwm2XTs5Co5uh85VqGBzLh7+cPNP0OlyWPwsLPgHJO0zVX6q0x4rMNw+lXIqWyXnjJDekLAVCvJtG4vUndRYaNoVPAJhyfOg/9aJiIiISANWj/9EKyIiIiIiIiJiex2bepW4XhOVzK+7ihNsYpMzrOdD2vuXW41m+oLil9HHTmbx6erYEvePncwi8WQWfaYt5am5O8qN50wC0NmW7knk/xbvo8sLv/LK/N0UFBS/kFwdlWw9b/3UAvYeTS93bZEGw8HBJI1YLHUdSdX1vwsKC2D9R7Zdd+dcwAKdr7Ddms7uMPkzGPggbJgNn48349VJ/AkIg6S9tk2YSIkyiVhVEdILctKLW19Jw5caYyogDXsSDq6FfYvqOiIRERERkWpTUo6IiIiIiIiIXFAcHCzEzZxgrYDz6ZpY/vHdthJznB0tbHx2JD6NTHWdZ8aHl1onOvEUoRHzCY2YT//py3jpl93M3XwYgFPZefSfvox+05eRfCrb+sykXs3x9XBh77SxJXIP8vILSqy95WBxVZ6PV8Vy+PhpwFTJ+eqPAyXmjvnXyir+BETEppqEQthE2PgpZJ+yzZqFhaatVKtB0DjYNmue4eBg2ldN+Cdkppg9vEOqvk5gGGSfhJPxtokrM9W0L/NrX7XnQnqbY/wm28QhdSsnA9KPgF8b6HWzqZy0dCrkl64cJyIiIiLSECgpR0REREREREQuSGcq4Dg5lq7MkZtfiL+nq/X6tsGtAejW3Jvlj11M39AmZa65OtpUsdkQl1rq3ic39+Gf13Rn83OjcHVyJPLlsUSMCwNgR3yadV5efgGfrYkr8ezT80ylna//LG6D9cHfe1vPs/PUtkWkTg24H7LSYNt/bbNe4m5I3gtdrrTNemXpOwXuWglXVbPCT0BRsmKijVpYnal041/FpBy/9uDiBQmbbROH1K3U/ebo2xYcnWHE86ZN2rb/1M7+GSnwTm84sLZ29hMRERGR856SckRERERERETkghXi487O+JPW65/uHwTAa5O6lZjn6GBhTcRwvr1rAG0CPPnu7oFlrjd3czxLdx8r1coqdsZ4RoQ3LTHm6uTIJR0DAThUVAkHYGtRO6s2AR58dXs/wCT7xJ84zcp9SQCsf2YEY7s0o1tzbwB2HE6jMhJPZrErIY3th0u3zDrbqew85m05TKEt29JUYNHOo6yJTq54okh91KKfqdjyx3tQUFDx/IrsnAsWRwi/vOZrnUuzruDdvHrPBhYl5STtsU1STAUvAAAgAElEQVQsKUVJOX5VbF/l4ADBPVQp53yREmOOfm3NMfwyaN4XfpsOOZn23z/qV0iJhn2/2n8vEREREbkgKClHRERERERERC5Y8SeKk2G2PT+abs19iJs5gWv6tig1N8THHTdnR+v10kcvZm3EcOt1v1BfAKZ8uZFVUSa55Lp+Lbihf0ssltLVeABa+LoDcCi1+EXjNxsOAfDV7f0Z0MbPOn7Pv80L52EdAwj0cgNg9k19AJj8wboKE2jyCwrpN30ZE95ezWWz1tD6qfnlVti5/uM/eOSbbdbPUZ4TmTkVJviU57e9icQlZwCQnpXL3f/exA2z/yTxZFa11hOpUxYLDLjPVPnYt6hma51pXdV6KHgG2CY+e2jkCx6Btq2U4+AMPq2q/mxIbzi6E/KyK54r9Zu1Uk4bc7RYYNRLpqXVH+/Zf/+oxeZ4ZKv99xIRERGRC4KSckRERERERETkgvXDfaYyTnhQY7wbOVfp2XaBngT7uDPr+p78cN8gHh/bscR9VycHZlzVjVeu7FruGo1cnAB4/de9pGXmArA/OYP+rX0J8XHHydGBly/vDMD2omo4T4wJsz4f2NjNer4h7jhgWl0tjzxWaq/dCSdLXBcWUmZlmsLCQuteN326niNpp9mVUHYlnh4vLeGyWWvIyq1a+6xPVsdy62cbGPZ/vxMaMZ+uUxeX+hwiDU745eDdAta9W7N1jmyF47HQZZJt4rKnwDAbVsqJNokYjk5VfzakNxTkmsQcadhSY0yyl6tX8VirgdBxPKx5y7SXspf8PIhebs6PbDP/oRQRERERqSEl5YiIiIiIiIjIBatHC1MZZ8GDg6u9xsRuwfRo4UPfUF9a+TWyjs+5p+wWV+V57sed/G/9QTYdOE7SqeJqD12b+5SY1ym4cYnrD2/sDcDXfx4gLjmD//x5kNs+38jna2KZsaD4ZXlqZk6pPQ+lniYrN5/Zq/ZzMiuXE5k5tH5qQYk5A2YsZ8Lbq+n47EJmr9pvra7z8cr91jl7j6ZX6bPuOXKy3HubDigpRxooRyfodyccWA0JNaiysXOOqRgTPtF2sdlLQDgk7bVN8kJyFPi3r96zIb3MUS2sGr6U/cWtq8424gXIOQUrX7ff3of+hOw0aDMMTh+HEwftt5eIiIiIXDCUlCMiIiIiIiIiF7zy2ktV1aOjOgAmIadLiHelnvnHaPPMT9sSiJi7A4CmXsUVcHq08OHVSabazq2DQks9P6ZzMy5q48vB1EzeXh5lHZ/6824+XLmfxPQsRr6xgps/XW+99851PQFYHpnIjAV7mDZ/D92mLqbHS0usc1ydSv61UXZeAdPm7+HDFSYZ55WzEn62x5tKOpFHT5ZZfeds7/0ezfebDpca/7GoatGna2LP+bxIvdbrJnDxrH6bnYIC2PUDtBsB7k1sG5s9BIaZRIm0QzVbJz/PtC3ya1e95xuHgGdTSNhcszik7qXGgG8ZSTmBYdDz77BhNqTa6b8TUYvBwQkGP2Ku1cJKRERERGxASTkiIiIiIiIiIjZyeY8Qdr80ht6tKv8y/f7h7bmkY0CJsX9P6V/i+m99WxI3cwIvXNq5zDU6NvVi39H0MivW9HtlGdGJp6zX+6aN49LuwQCs2JfEF+sOlLnm8n8MK3P87WUm8WdEWCDuzo74NHJmx+ETFBQUMvZfq7hh9p8UFJRfNeO1RXsBeH1yN16f3I0nx4ax+JGhdG9RXBFo8vtry31epF5z94GeN5pqNycTqv784Q0mwaXzVbaPzR4Cws0xMbJm65w4YNpPVbdSjsUCwb1UKaehy06HU8fAr03Z94c9bZJmlk+zz/5RS6DlAGhxkdnnyDb77CMiIiIiFxQl5YiIiIiIiIiI2FAjF6cqP3PXxcVVAfq19sXRoWqVezo2a0xGTj67EspvC3WGS1EFnHuGla5EEODlCkDky2MJ8XEndsZ4lj46lM3PjbLOySsopLCwkOOZOfRu1YRuzX34duNh2jxd3PbqivfWlLt/1xBvHB0sXN2nBVf3acE9w9rSoakXACseHwbAxgPHycjOq/iDi9RH/e+CgnxY/3HVn901F5zcoOM428dlD4Fh5pi059zzKpISbY7VrZQDENIbkvdBVlrNYpG6c6YCjm85STmNg2DAvbDze0jYYtu90w5D4i5oPwqc3UzCWU3a0ImIiIiIFFFSjoiIiIiIiIhIHbuojR8bnhlJ7IzxfHvXgCo/37GZl/U8YlwYc+8dyBvXdOejG3uXmBdYlHQD8OTYMOv5Rzf2Jm7mBNY8OZydL47BzdkRMG292gV64evhQtzMCcy8yrTRWrYnkc0HT+Dh6si4Ls1KxbP9cNkvxQsLC9kRn8bI8MAy77fy87Ce/7A1vqKPLVI/+baG8Imw8RM4tqvyzxXkw655JinArbH94rMl9ybg2azmlXKsSTnVrJQDENLLHJVI0XClxphjWe2rzhj0ELj7wpIXoLD8qmxVFlXUvrH9GHMM7m7aV9lyDxERERG5ICkpR0RERERERESkHgjwcsViqVqFnDM6NPW0nt88IJReLZtwVa/mjAxvir+nC9f2bUH0K+NYGzG8xHPrnhrOm3/rzujOJrHGxckBT9fyK/0M7WDabE35ciMAufmFXNu3Bdf3b1liXpC3G2mZuYRGzGfxrqPW8XlbTKLNvmOnKM+MosSfk6dVKUcasBEvgHMj+GQMRC+t3DMH1pjWPV0m2Tc2WwsMq3mlnOQok+Dj4Vf9NYJ7mqNaWDVcKWeScsqplAPg5g0XPwGxKyBmme32jloC3i0hoKO5DuoBmSlwUgmiIiIiIlIzSsoREREREREREWngvNycWfn4JUS/Mg53F0fruIODhY3PjmLmpG44OTrg5Fjyr4KCvN25smfzSu8T7ONe4nrW9T2xWCxMv7IrG54Zydx7B/LIyA4cScvig5Xm5eqLP+8G4HROPo9+uw2A5yaGl7vHtX1b0LyJOzviT1Q6robm2MksftubWNdhiD35t4cpy6BJK/j6Gtj4WcXP7JwLzh7FlToaioBwSNoLBQXVXyMlumZVcgAa+Zr2V5s+t31rI6kdqftN5SVXz3PP63Mb+LSCJVNNhamaysuG/b+bKlVnkmODepijKi+JiIiISA0pKUdERERERERE5DzQ0q9RqaQbe3j/hl7W80YuxVV1Arxc6dWyCW0CTAuq9383STnxJ05zNC3LWiUHYHhY03LXt1gstGjSiAU7jlJQcH60DcnKzSfxZJb1uv/0Zdz62QaST2XXYVRid94hcNsiaHsJ/PIwLHm+/MSV/FzY/SN0HAcujWo3zpoKDIPcTEg7WP01kqNMIlNNXTbL/Cxnj4RV/7RNwobUnpQY8DtH66oznFxhxPNwbAds/7bm+x5YA7kZ0H508VizLmBxNC2sRERERERqQEk5IiIiIiIiIiJSaWO7NDvn/SBvt1JjyyKP8cNWk5Tz6qSuFe7RwtdU5Hl98V6u/mAtoRHzG3SCzgP/3UK/6csIjZjPlC82WsfXxaTUYVRSK1y94LpvTGWPNW/B97dC7unS82JXwOlU6HJV7cdYUwFFla8SI6v3fNZJOHXUVLmpqVYD4J41EH4pLHsJPp8Ax+Nqvq7UjtSYc7euOlvnq0w1m99egdysiuefS9QScHSF1kOKx5zdISAMjmyr2doiIiIicsFTUo6IiIiIiIiIiFSaxWJhx9TRbH1+VJn3Q5oUt7ia/+BgAJ6Zt5P1sakA/K1vywr3eP7SzgB8u+EQG+KOA7DtcMl2Vo9/t43QiPm8VNQeqz5JPJlFaMR8PlwRw4wFe1iy+5j13tI9xecP/HcL7/4WXRchSm1ydIIJb8DoaaYazheXQUZyyTk754KrN7QbWTcx1kRAR3NM2lO951OKfg3YolIOmDZWkz+DKz+CY7vg/cGw9T9Q2HAT+y4IWSchI6nySTkODjDqRUg7BBtm12zvqMUmIcfFo+R4UHfTvkrfHRERERGpASXliIiIiIiIiIhIlXi5OePTyKXMe0He7ux8cQy7XhxD52Dvaq3v6WraYqVk5FjHnpyz3Xp+7GQW3206DMCna2KrtYc99Zu+DIAZCyP5cOX+Muc0cnEE4PVf9xKXnFFrsUkdsVhg4ANwzRdwdDvMHgFJ+8y9vGzY8wuETTBteRoadx/wCqp+pZwzSTl+NkrKAfPz7v43UzUnqBv8cA98dzNkptpuD7Gt1KLfKyvTvuqMNsMgdAisexfyciqaXbaUGPMdPLt11RnBPSAjEdKPVG9tERERERGUlCMiIiIiIiIiIjbm6eqER1FizZTBra3jc+4ZWO019x07RWjEfLpO/ZX+RUkvZ3y/6TDZefnVXrs2zLq+Jx/8vZf1+q1re1rPNx44XhchSV3odDnc/Atkn4JPRkHcaoheBtlp0GVSXUdXfQFh1a+UkxwFFgfwbV3x3KryaQk3/wwjp0LkAnhvgPl5S/2TGmOOvlVIygEY9BCkJ8DO76u3b/RScyyrSlVQd3NUCysRERERqQEl5YiIiIiIiIiIiN08O7ETkS+PZc9LY+ndqkmln3v5ii7W83uGFb+kTc/Ks55/e9cAAP7x3TY6PruIzJzie3UlNGI+AHcMKU4waNLImVGdmjK2SxAA1/RpzsC2ftb7MxdGkpNXAMCPW+OZvWo/ielZ/BaZWIuRS61p0RfuWAaegfDlFbDsJXD3hTYX13Vk1RcYbir/FBRU/dmUKPBpZb8qQQ6OMPgR8zN384Z/XwULn4Tc0/bZT6onpahSTmXbV53RbiQEdoK171SvzVTUYvBrV3aFnmZdTcJYwtaqrysiIiIiUkRJOSIiIiIiIiIiYlduzo64F7VrqqzLewQzvmsz1j01nCfHhpW6/9a1PUol+XR6/leOncyqUaw1cTqnuFrPPcPaETdzAnEzJ7Dl+dG4OpnPv3/6eF6d1A0PVyfiZk4AIPlUNh2eXUhWbj4P/W8r0+bvod8ry7j18w2ERszneEY127JI/dUkFG5fDC0vMhVmOl0Gjs51HVX1BYRB3mk4EVf1Z1OiTVKEvQV1h7tWQL+74M8P4OPhcFpVquqN1BjwCgaXRlV77kxruMTdxVVvKisnE2JXld26CsDFA/w7wBEl5YiIiIhI9SkpR0RERERERERE6p3Gbs68d0NvgrzdAXhmfDgAdw5tw+6XxnB5jxAcHSxc2j24xHP9py9j5b6kctfddyydGz/5k5NZuVWOafaq/SzaeYTth0+Uef/7zYcBuGlAK3w9XMqc4+BgwWKxWK8n9WpuPX9rWVSZz3yyOpbc/GpUIJH6zb0J/H0ujHsdLo6o62hqJtD8+iQxsmrPFRRASgz4t7d9TGVxdofxr8H130HyPpj/WO3sa2/7FsNrbczPsqFK3V92tZrK6DLZJPSseatqz8WuhPzs8pNywCRzqX2ViIiIiNSAknJERERERERERKTeu2NoG+JmTuDp8eE0cnGyjv/rbz3Y89JY3r+hl3VsTlFyTFk+XR3Lqqhk5m2Or9L+s1ftZ9r8Pdz9781cNmsNn66OLTVndZRJBnp2QqdKr/vPa7pbz9//veQL9bev6wnArN+iaf/MQgqr05qlljz0vy3c8tn6ug6j4XFygf53QuOguo6kZgI6mmPSnqo9l54AuZm1UynnbB1Gm0SonXNg+3e1u7c9bPoMMlNg6Qt1HUn1pcSAb+uK55XFyQUuuhviVkHClso/F7UYnD2g1cDy5wT1gPQjkH6serGJiIiIyAVPSTkiIiIiIiIiItJgOTpYcHdxZFzXIO67xFRZ+HFrAhviUsuc37SxGwBbD5Vd7WbTgVRSTmWXGp82v2SywUu/7KbnS4v5LTIRgP/7dS+/7jpG+0BPXJyq9lduf6324+XqxFvX9uCyv4yfyKx6dR97WxeTwmuLIvlxawK/702ioKD+Jg6JHbl5Q+OQqlfKSS6qDlVblXLONvgRaN7PVMtJKz+Rr947fRyiloBnU9jzM8StqeuIqi4rDTKTwbealXIAet8CLl6w9p3KzS8sND+3NsPAybX8ecE9zFHVckRERESkmpSUIyIiIiIiIiIi54XHx4Qx7YouAFz9wTqy8/IB+HXXUZLSTaLNiqLWVvO2xJeqPHMqO49J76+j97SlJJ7Mso4fTcuiLMczc7n18w2sjkpm1m/RAAxu71/luP/1tx5EjAvj+Ymd+PauASx+dCiX9wgB4MHhxRVEer68hF0JaVVe314ysvO47uM/eO+sCj8HUzPrMCKpUwFhVa+Uk2J+3eBXB0k5jk5w1YdQmA/z7jattBqi3T9BQS5c/QU0bg6/Pt3wPsuZtlvVbV8FJjGszy2w6wc4fqDi+Ul7Ie0gtB917nnNugIWOLK1+rGJiIiIyAVNSTkiIiIiIiIiInLeuKF/S3q08AHg2o/+YNuhE9z11SYe/da8UD18vDhp5Os/D5Z49ux718/+03o+cOayEvO8XJ1KXP/9k+K5/Vv7VTlmRwcLd1/cltsGt6Zfa1+CvN2t9x4d3ZENz4y0Xk94e3WV17eXMf9aWWpsR3z9SRqSWhYYbirfFORX/pnkKHDxBK9m9ovrXHzbwNgZpu3RH+/VTQw1tfN7U2Gm5UUw8gWTPLL9m7qOqmpS95tjTSrlAPS/ByyWyv27jFpsjhUl5bh6mfZqCUrKEREREZHqUVKOiIiIiIiIiIicNywWCx/d2BuALQdPcPNn6wFYFZXMjIV7SD6Vw22DWgOQmpFT4tmoY6es59GJpygoKCQnr4AzHZnm3juQz2/ty7QrTTWeq3qGlHj+zqFtGNWpqc0/U4CXK638Glmv10Ynn3P+icycc963hWMnszh8/DQA/xjdgQ/+3gsXRwcl5VzIAsIgLwuOx1X+mZQok/BgsdgtrAr1vBHCJsKyF+HYrrqLozrSj0LsKug62fwMu0yG4F6w7CXIyajr6CrvTKUc39Y1W8c7BLpeDZu/hMyyWxhaRS2Gpl3Au3nF6wb3UPsqEREREak2JeWIiIiIiIiIiMh5JcDL1Xru4VJc1ebDFaYaw7COAfh7uvLGkn2ERswnK9dU9jiTZPLIyA4AvLM8mpkLIwEY2iGAni18GNYxkEu7BfPWtT144dLOJfZ9enw4jg72SS74350XWc/PruJztts/30BoxHx6vLSE/UmnypxjK1sPnbCe3z+8PWO7BJFXUMBHK/ezYMcRu+4t9VRguDkmVqGFVXI0+NdB66qzWSxw6Vvg5gNz7oC87LqNpyp2zgUKTTIOgIMDjJkO6QmwdladhlYlqftN6y1n94rnVmTA/ZCbCRs/KX9O1kk4uK7iKjlnBHWHk4ch49wJkSIiIiIiZVFSjoiIiIiIiIiInFcsFgsfFlXLiT9xutT9vqG+BPu4Wa/PVHdJOZVNIxdHpgwx1RreXLqPT9fEAvDy5Z2xFFXzcHCwcHmPELwbOXPvMNNu5f0betnvAwFB3u7Ezhhvvd5z5CTZecVtgtIyc1kWmWi9XhVlv5fH2Xn53PXVJgDWPTXcOv7a5O4A3Pv1ZrvtLfVYQEdzTKpkUk7uaUg7BH51nJQD4OEPl8+CxF2w/OW6jqbydn4PzbpBQIfisVYDoNMVsOZfcLKBJMilxtS8Ss4ZzbpA2xHw50eQm1X2nP2/QUEetB9duTWDepijWliJiIiISDUoKUdERERERERERM47w8MCreeX9wjm4ZHmxf/DI9vj7uLIi5cVV7mJPJoOQFxKBk0bu+Hh6sRfNW/SqNQYwBNjw4ibOYFxXYNsGX6ZLBYLn97SB4Bxb62i47OLyC8oZO/RdLq/tLjE3Bd+2kXKqXNX/Hjv92jWxaRYrw+kZJCRnVdhHH/sN21hQnzcCfIurmwxuXdza5WitTHFSUE/bo0nNGI+X/95gNM5+ch5ytULvFtAYmTl5qfEAIXg386uYVVahzHQ5zZTYSZ2VV1HU7HU/RC/ybSu+quRU03SyfJptR1V9aTEgF9b26036EHISITt/yv7ftRicPWG5v0qt15QN3M8oqQcEREREak6JeWIiIiIiIiIiMh5x9nRARdH81dftw5qzcMjOxA3cwIPF7Wm6tmyibXyzHM/7CQpPZulexLp1twbgIs7BABwQ/+WfHJzH7u1paqqwe0CSlzP2XyYMf9aab1+dFQHerdqYs6/3cbxjBx+3XW01DqFhYW8tmgv1338B6ER84lOPMXFr//O6DdXlpr7VwdTMgD44ra+pe69ckUXAK7/uLjF1kP/My+yn5m3k8tmra5wfWnAAsIgqbJJOdHmWB8q5Zwxehr4toF5d8PpExXPr0s75phj56tK3/NtDf3vhq1fw5FttRtXVZ0+DqdTwdeGSTmtLzYVhNbOgoKCkvcKCyFqCbQbDo6lEzDL5OZtvhdKyhERERGRalBSjoiIiIiIiIiInJdWPDGMJ8Z2pHtRos1fnWlHBdD3laUA5OabF7gf3tibtRHDeeXKrowIb2r/YCvJxcmB9U+PwNnRxP7E99ut9355YDAPjmjPN3deBMCKfUn0fHkJd321icT0km1c0v9SEWfkGysA0+7rraVR1p9DWeZticfBAm0DPEvdaxPgYT3Pys2nsLCwREJTVOIp3l4WVdmPKw1NYBgk74P8Ciou5eeaKi9g2wopNeXiAVd9DOlHYMHj9tsnOcokh1RXYaFpXdVyIPi0KHvOkMegkS/8+kzN9rK3lP3maMvvgcUCgx6ClCjYt6jkvaPb4dQxaD+mamsG9YCEep7gJCIiIiL1kpJyRERERERERETkvBTk7c69w9qVSL75q9sHty5x7VA0183ZkWAf97IeqXOBjd2IemV8ibFv7xpAlxCTfOTk6MCYziUTifq9soyRb6zgounLmL1qPwdTMgHw93Qptf6bS/cxa3l0mXtP/WkXmw+eoKCQMn+ubQM86VoUx6+7jpKSkUN+QcmEgDeW7KvkJ5UGJyAc8nPgeGzxWF42JGyBTZ/Dzw/DR5fA9BBY+7apPuLiUe5ydaJ5b7j4SdjxLeycY/v14zfBrD6wYXb11zi2y1Qk6jqp/DnuPjDsKYhbBXsXVH8ve0stSsqxZaUcgE5XgHdL8z07W1RRq792I6u2XlB3SDsImam2iU/Enmqa+CciIiI2paQcERERERERERG5YD03sVOJ64hxYXUUSdW9fV1P7h3WlgUPDqFfa98S9wa18y81PzrxFEdPZjFt/h7+9uE6AD67pV+Za/9n/UGenreDwr+81Pt8bdw5Y7JYLHx6i2lrNWdzvLWSz0c39mbvtLG4OZu/jsw7RyUeacACi379rH0HfnoAPhhiEnA+GgY/PwQ755oknH53wKRP4NaFdRpuuYY8BiF94JdHIC3etmvv/tEcf58BWSert8aO78DiaBJPzqX3reDfERY/B3k51dvL3lJjAAs0CbXtuo5OMOBeOLgODm0oHo9aAsG9wDOg/GfLEtzDHKvTwurQBninD6Qfq/qzDVluFmz+Eo5sr3iu2M6O703iX8zyuo5EREREiigpR0RERERERERELmhz7hkAgKerE82bNKrjaCrvsu7BPDE2jE7BjUvd+3v/VrxxTXf2vDSWGy9qVep+Rk4+ULLdVJ9WTZgyuDW+Hi4kpWfznz8PciTNtL06lZ1Hn2lLrXM3Plt+lYkAL1cAVu5LYnlkIgAD2/nj6uTIP682L7Yf/N+Wqn5caQgCwsDJDTZ/AXt+hkZ+MOA+uPpzeHALRByAW36BMa9A18ng1ayuIy6boxNc9ZFps/XDPVBgoySywkLY84upEJSZAmveqt4aO+dC20vAo3TyXQmOTjB6mkl82fhJ9WK2t5QY8G4Ozm62X7vnjeDmA2uLfs6ZqXB4A7QfXfW1grqbY0I1knJWzDSttA6vr/qzDVF+rqmM9U4vk5z3432q2lJbcjJMEh6YBDQRERGpF5SUIyIiIiIiIiIiF7TerXyJmzmBnS+OqetQbMbBwcJVvZrj7uLIy1d0KXeeh6sTe6eN5a6hbfjklr48O7ETNw0oTuJJOZVDbn4BXV74leRT2QA8PqYj/p6u59x/6qUlKxB5ujoBMCI8EIAFO44SGjG/Wp9N6jEXD7hnLTy8A56IhZt+gFEvQucrTSLKOVrJ1Tt+bWHMdIhdAVu+ss2aSXtNgsyA+6DLJFj3Lpw8UrU1Dq03bZS6Xl25+e1HQdvh8PvM+tl6KTXGfDfswdUT+t5uEqFSYiB6GRQWVC8px70J+LSCI9uq9tzRnRBdlNB4bHfV921ICgpg+3cwq6+pjNU4GPpOgaPbzfdW7G/1m5CeAD4tYf/vdR2NiIiIFFFSjoiIiIiIiIiIyHku6pVx7J8+nj+eGmEd2/CMqXbj6uTIU+PD8XZ3BuDB4e2tbaauen8NS3eXbLly98VtK9zvlkGtiZk+HoAxnZtax92cHa1rA8QknarmJ5J6y6+teSHckBJwytP7Fmja1bTgsYXIX8yx4wQY/hwU5MHv06u2xo7vTDWisAmVm2+xmGo52Sdh5etV26s2pMSY74y99LsTHJ1h3SyIWgyN/CG4Z/XWCu5R9fZVa98GF0/wbAaJ52lSTmEhRC6ADwbD3CkmOe+6b+D2JTDyRXD1hvUf1XWU57/jcbDmbZOw13cKJO2petKfiIiI2IWSckRERERERERERM5zzo4OODhYaObtxrbnRxM7Y7y1zdRfOThYWPTQUABy8wu55+vNANw8oBVxMyfg6FC5ZAtHBws7po7mjWt6lBj/5s4B1vMR/1xBZk5edT6SiP1ZLKbNVvxGSN1f8/Uif4GQPtA4CHxbQ787YMu/IXFP5Z7Pz4PdP0CHMeDqVfl9m3aGXjeZxIjk6OrFbg+ZqZB1AnztmJTj1Qy6/Q22/geifjWVgxyq+VokqIdJfDh9vHLzTxyEHd+b5K6QXpX/99yQ7F8Bs0fC/66DvNMw6RO4axV0HGt+/bh6Qs8bzPc2/WhdR3t+W/wcODiaRKg2l5ix2BV1G5OIiIgASvFWfAIAACAASURBVMoRERERERERERG5oHg3csZSQRWTkCbupcZeuLRzlffycnPGo6h11RndW/gw/8HB1uvLZ62p8roitabLJHPcOadm66QdhoQtJSvcDH0cXLxg6dTKrRG7AjKSKt+66myXPGMq7Cx5vurP2suZRCd7VsoBGPgA5GVBVppJyqmuoO7meGR75eave88kplx0DwR2gpRoyMuu/v71yeGN8MVl8OVlkH4ELn0b7ltvktj+mvTUd4qpCrXp8zoJ9YIQuxL2/ASDHwXvEGjaxVSFivmtriMTERERlJQjIiIiIiIiIiIif+Hs6EDczAmsiRgOQPtATxwqWSGnMtr4e1rPoxJPUVhYaLO1RWzKpwW0HGAqntTke7p3oTmGTSwea+QLQx6BfYsgdlXFa+z4HlwbQ7tqJJZ4BsKQR2Hv/MrtVRtSYszRt4199wnoCB3GgcUR2g6v/jpBRVW/KtPCKjMVNn9hEqi8m0NgOBTmQ3JU9fevL/54H2aPgGO7YMwMeGAz9L7ZtAkri19b853d+Cnk5dRurBeC/DxY+KRpGzjwfjPm4ABtLob9v9fs9y0RERGxCSXliIjI/7N331FRnlsbh39DR0Gs2BEVC/aosRtrrNFoYnqP6f2kmnoSU0w5yZfeY4rGFDWJUew99t5BRcCuqCiKSJ35/ngiiLSZYWAA72st1jvzlufdA4NZcW73FhEREREREclT3cr+xL09lHlP9nLpuv4+nsSOG5L1/OnJW7j3p3VkZFpdeh8Rl2h1LRyLMiEEZ0VOh+pNoUbTnPs7PwCV6sK8l8FawPs/PcWMvwofBt5+ztXQ5SEIqGlCFaVBwh6weECV0OK/17AP4bY/wL+K82tUrAZB9eHw5sLPXfsdpCebLj1gOuUAxO9w/v6lwdpvYfYY8z58fBN0fci+92On+yDpqOnmItnSzsL4wbD6a+fXWP+9eV8NeBO8L+hy16g3JB0xf3aJiIiIWymUIyIiIiIiIiIiIiXOYrGw+oV+AEzdcIB5O44S9uIsdc2R0qflSNNlZdsU564/dxLiluUcXXWetz/0fcmMttrxZ/5r7J4Lqaezx2k5w9vfdG7ZPdd0cnG3E3tMFxkv3+K/V2AtE1Ioqtpt4VAhnXLSz8HqL6HJAKj579i/amHg4VW2QzkbJkDEU9B0EFw7HnwD7b82rD9UaQhrvim++sqiLb/BvhUw6xlY/ZXj1ycnwKI3IbSnCUpdqFEfs41ZXOQyRUREpGgUyhERERERERERERG3qFkpd4eFR37ZSMJZjTiRUqRidWjcB7ZOdW4UzK65ZnTRhaOrLtTmBqjZChaMzX+8z9bJULEGNCxi16o214M1HXb8VbR1XCFhD1Rt7O4qHFOnnak7JTH/czZNguTj0P3x7H1ePlCtCcRHFn+NxWHL7/D3o2b813U/mtfjCA8P6HQv7F9lX6ehS4HNZoI4tdqYPxtmPWs6ETli8TjzXhz8DlguGjFZub75/dqzyHU1i4iIiFMUyhERERERERERERG3eah3zg/lI7Ycpv3r8ziRlOqmikTy0GoUJO6D/WscvzZqOgTWhjrt8z7u4QlXvgYn42Dd+NzHU07DrjmmY4+nl+P3v1CtNlCjuQlZuJPNBidioFoZC+XUbme2R7bmfdyaCSs+gbodoEH3nMdqtiibnXK2/wl/3g+hPeCGn50fn9buFvCuAGuKMKqpPIldYkZLdXkIRn0PTQebTkTrf7Dv+qM7zJi0jndnd2S6WOM+pktXZrrLyhYRERHHKZQjIiIiIiIiIiIibvPsoObEvT2UuLdzjva59bs1WK0aZSWlRPOh4OXn+Air9HMQvQCaDTHdQvLTuJ8Zr7TkndxdWKIiIDPVBIOKymIxI6z2rYSTe4u+nrOSEyA1sex1yjkfyslvhFXkdDgZa7rkXNy5JDgcTu2D1DPFW6MrRUXA1HugXie46VfwqeD8Wv6VTVeorVNKx/g0d1v1pel+1eoa03no+h8h7EqY/gRsnFjwtTYbzH7OjBDr82L+5zXqDeln4cBaV1YuIiIiDlIoR0REREREREREREqFWY/3zHocefg0r0eUwa4SUj75VYKmA03XkMwM+6+LWQzpySbUUxCLBa4cC+cSYNmHOY9tnQyVQ6B+J4fLzlPr67LXdZeEPWZbtZH7anBGQA2oVBcO5xHKsdlg+UfmNeU1qiy4hdke21m8NbrK7nnw+x1Quy3cMhl8A4q+Zqd7ISMFNvxU9LXKsoQY2DUbOtwFXr5mn5cv3DDRBGmmPQKbf83/+qgZELsU+r4EFarmf15oT7B4aISViIiImymUIyIiIiIiIiIiIqVCeO1KxI4bkvX8++Vx7DpahrpKSPnWahScPWbGztgrcgb4BpkPxwtTu63pJLLqc0g8aPadPW6CPa2uzd15xVlVGkBINzPCyuamblQn/g3llLXxVWB+Toc3594ftwwObYBuj5qRZBcLDjfbo9uLtz5XiFkMv95iar51qgmluULNluZ3Ye13ZtTXpWrNt+Y9cvnonPu9/eDGSdCwJ/z1oOkqdLH0FJjzogl5dbir4Pv4VzZj82IWu6x0ERERcZxCOSIiIiIiIiIiIlJqWCyWHKOsBvzfUjdWk9OK6OOEjonghT+3kpFpxeauQIO4R5MB4FsJtk217/zMDNg5E5oOMONp7NHnRbBZYdFb5vn2P8GW6ZrRVRdqcx0c3wlHtrh2XXsl7DEdPCo3cM/9i6J2Ozi+O/cYquUfmXFEbW/K+7rKoeBdAeIji73EIolbDpNuhGphcPs08K/i2vU73QuJ+0ynmEtRahJsnAAtR0JgrdzHfSqYUWEhXeGP+8yfARda+Smc2guD3gZPr8Lv17gPHFyfeyyeiIiIlBiFckRERERERERERKTUebxfk3yPWa02/t58iOQ0B8YIFdH/5uzk5m9XAzBp9T7CXpxFw+dnltj9pRTw9oPwYRA53XSrKMz+1WYcVWGjqy5UpQF0ug82/Ww6qmydAjWamw4jrtRiBHh4m2457pAQY0Zy2RtWKk3qtANscGRr9r4j2yB6HnS+H7z9877Ow8P8LONL8Vi+/Wtg0vVQub4J5BQ0GslZzYaaEWBrvnb92mXB5l8g9TR0fiD/c3wqws2/Q73LYcpo82cOwOlD8M8H5s+hRr3su1+j3ibYF7esqJWLiIiIkxTKERERERERERERkVLnif5NqBPkB8CGfSez9q/fe5JGL8zksV820uKVOaRmFO8IlHEzIxny0T98uig6z+O/rNlXrPeXUqbVteYD9d1zCz83agZ4+kJYf8fu0fMpMy5o2iOwfxW0HuW60VXnVahqOv9sneKeMUIn9kDVMji6Csz4Ksg5wmrFJ+BdETqOzvua84JblN5OOYc2wsRrISAYbv8bAmoUz308vaDj3Wak0rGdxXMPgLSzkHSs+NZ3htUKq7+Cuh2gXseCz/UNgFsmQ932MPku2DkL5r8K1gwY8Ib996zXyXRo2rOoSKWLiIiI8xTKERERERERERERkVLHYrHw0+jOAPy54SAA6ZlWrv1iRY7zxs2MKrYaEs+l89XSGHYcPg2Av7cnK5/vm+Oc5//YmtelUl417GVGFG2bUvB5NpsJ5TTqDb6Bjt2jQlXo+TQc2mCet7rWmUoL1+Z6SDoCsSU8Is5mM51yqpXRUE5gLQioBYc2meen9pv3Q4c7Cu8sExwOZ+Ph7PHir9NRs8aY9+od06FS7eK9V/s7wNMH1nxTPOufPgRf94b/NYEfroL1P8K5k4VeVuxiFsKJ3dD5QfvO96sEt06FWq3ht9tgy2/Q7VGoEmr/Pb18oEF3E4ISERERt1AoR0REREREREREREqlsOAAOjSows4jZwCIjk/KOnZ394YA/LAiDpvN5vJ7f744mrav5eyGsvGVK6kdZEbTVA/IHrsTOiaC+NN2jDOSss/TC1qOhF1zIOV0/ucd3Qan9jk2uupCne6DoBDT5aJqI+fWKEzTQeBbqWgjrDLSTLAiOcH+a84eN92Giut1lYTabeHwv6GcVV+YoFGXhwq/LjjcbEvbCKszR8y4tQ53QlC94r9fQA0TNtv8S8G/R844tQ++H2yCOV0fhjOHYfpj8F4T+OUm2DYV0pJde097rfoSAmpCi6vtv8YvCG77w4ywCwqBHv9x/L6N+5gwUOIBx68VERGRIlMoR0REREREREREREqt1nWDWBOXQHqmle2HzIe385/sxSvDWuDx70Sfhs/PJOFsmkvv++7s7LEq/zzbh5i3huDn7QnAXw93J+Kxnjw9oGnWOTd/u9ql95dSrNUoyEiBqIj8z4mcAVig2RDn7uHtB6PnwI0/O3e9vfdoMRwi/3Y+pLDyE5j5NMx61v5rEvaYbVkdXwVQpx0c3wWJB2H9D2bEWOX6hV9Xs6XZFnWElTUTUs8UbY0LRUUANggf5ro1C9PpXkhLMsEcVzmxB8YPNl1xbp8GA9+ER9bBfYuh8/1mRNeUu+G9MJh6L+yaC5nprrt/QY5HQ/Q8M+LMy6fw8y/kXwXuWQAPrTBjrRzVqLfZqluOiIiIWyiUIyIiIiIiIiIiIqVWoxoVAXhl2nbGL4vFwwKh1SoAUD3AN+u8Fy4aI5WakcmJpFSn71u/qv8FjyvgcT4BBLSrX5malfx4pG8TPru5PWC6+BxPSiU5LaNYOvdIKVK/E1QOKXiEVVQEhHQxHUGcVakOBAQ7f7092txgghG7Zjl+7cm9sOQ98KsMWyfDvlX2XXfi31BOWR1fBVC7HdisMOM/kH4Wuj1m33UBNU3AoqidclZ+Ch+2gdSkws+1R9QME5Kq0dw169mjbgfzteZrsFqLvl58FHw/BDLOwR0zoF5Hs99igTqXmYDOf7abY61Hwe65MOk6+F9TmPWc6fpUnNZ8bUZ2dbzLues9vRwfhXdecAvz3tuzyLnrRUREpEgUyhEREREREREREZFSa0jr2gD8smYfOw6bTjlenuavNWc82iNrjNTs7Uf4YO5OrFYbe44l0eyl2XR4Y77TAZlAX2+a1wpk8ysDCjxvaJvavH1NawDemRVFi1fm8OnCaKfuKWWExWJG7+xZZEYxXexkHBzd6vzoqpLUoAdUquvcCKvZz4PFA0bPg8A6JthgT7giIQYsnibYVFbVbmu2u+dAWH+o1cq+6ywWE5AoaqecnbPgXALsnFm0dcB0lYldCuFXmfpKUqf74UQ0xBQxLHJ4C/wwBLDBnRFQu03e53l4QsOeMPxjeHo33PQrhHaH1V/CzgI6XxVVymnY9LP5c6O4g3Z5sVhMt5yYxa4JQImIiIhDFMoRERERERERERGRUuvCbjgA1gsyNsGV/Fj30pVZzz9eGE2Tl2bR7/0lWfuu/L+ljP5hLaFjIpi97bBd97TZbOw4fJpGNSoSVMG70POHtjHBocnrDwDw/rxddt1HyrBWo8CWCdv/zH3s/FirshDK8fAwQYHo+XD2hP3X7ZxlQgy9n4MaTeHKsXB4E2yaWPi1CXugSgPwLPx3q9SqVAcq/tsFqfvjjl0bHG5COc521Eo7CwfWmsdbJzu3xoV2zQVrBoQPL/pajmo5AipUhzXfOL/GgfXw41Xg5Q93zTLfX3t4+UCzwTDqe/CuAHtXOl9DYTb9bDpSdb6/+O5RmEa9Ifk4xG93Xw0iIiKXKIVyREREREREREREpFSb+mBXwoID8j3+QK/sMTiZ1pwfdEfHJ7EgKt6cN3EDH8zdyU1fryJ0TATxp1NyrXU8KZXNBxIBqOjjZVd9gX65wwUTVsbZde2Fth9K1OirsqJmS6gRDtum5j4WFQHBLaFqo5KvyxltbjChjO1/2Hd+WjLMetaMOurykNnXehTU7wILxkJKYsHXn9hjRiWVZRYLNLwCQrpBaE/Hrg0Oh9TTkHjAuXvvW2l+XnUug+gFeXdrckTUdAisDXXaF20dZ3j5Qoc7Ydds02HKUXtXwE9Xm5Fgd810biSapzfUuxz2rXD8WntYrbD6K6jf2fzM3KVRb7ONWey+GkRERC5RCuWIiIiIiIiIiIhIqdahQVXmP9mLL29tz4xHe+Q6/vSAprw7qg2T7u2ctW9gy5p0bVQt17kfL4xmZYzpCNLprQWEjolg/LJYAP47bRsd35jPiM+WAzCyfV27a1z1fD/u79WIEe3qAPDytO0kJqfnea7NZiN0TAShYyI4eOocj/+6kdAxEQz9eBlfL40B4I8NB9iw76Td95cSZrFA62tNQOLU/uz9Z4+bfWWhS855tVqZkUr2jrD65304tQ+GfpDd7cZigcHvmNe/5N38r7XZzPiqshJYKsg138Dt0xwf+RTc0mydHWEVuxQ8vGHwe/l3a7JXWjLsnm/erx5u+rio491mDNrabx27bs8imHgtBNYyHXKqNHC+hgbd4Mg2OHfK+TXyEz0PTsa6t0sOmO5O1ZuZ75uIiIiUKIVyREREREREREREpEwY1Ko2reoG5drv5enB9R3r07lhNa5pX5dJ93bmq9s68st9XbLOqVfFP991x87YQeiYCH5cuTfH/uBA33yuyK1WkB/PDw7nwxsvo6KPJwBtx84lI9Oa69zth05nPe7+9kKmbTqU9XzcrChS0jN58vfNXPP5Ck6eTbO7Bilhra412wu75eycBTYrhF/lnpqc1eZ6OLAGEmILPu/4blj+EbS9CUK75zxWpx20vw1Wf2nOy0tSvBnj40xHk9LGw9OMQHJUcHOzjd/h3H1j/4F6HaH+5SZMtXWKc+sA7FkIGecgfJjzaxRVUF3z+7L6K/huIEx/3DyOXQpJx/K+ZtccmHQDVGloOuRUqlO0GkK6AjbYv6Zo6+Rl9ZcQWMc948Eu1riP6S6UkeruSkRERC4pCuWIiIiIiIiIiIhIueDpYeGD69vRrXH1rH0vDgnnod6NWfZcX6LfHJy1v3pA4R+mV6tofyjnQsvH9M16HPbirFzHZ207XOD1zV+enfVY3XJKsaqNoG5H2HZBKCIqAoLqQ6027qvLGa2vM9utk/M/x2aDiKfApwJcOTbvc/q+At4VYPbzeR9PMJ2gyvz4qqLwr2JCGs50yjl3Cg5vMqOzwIwN278KTu4t+Lr8RM0Av8rQoHvh5xanQW9D+9tNx5ztf5nxaD8Og/+FwbuN4YerYOYzsG48rPkGfr0ZaraAO2dAQHDR71/vcvDwcv0Iq2M7TfDp8tHZXaXcqVFvE8Lav9rdlYiIiFxSFMoRERERERERERGRcuveKxrx7CDTmcLL04MXhjTnqSubsu6lK4kdNyRr3NR5n9/SPutxkL9zH6JWrpAz8BM6JiLH8/0J5wBY8FQvAn296B5Wja2vDuD5wc1zrbX3RDKb95/iXFqmU7VIMWs9Co5sNR++pyaZD+CbD3V8pJG7BdWDBj3MCCubLe9ztk2F2CXQ75X8gxABNaDXc2Zkz645uY8n7DHbauVgfFVRBIc71yln7wrTiel8KKfVKLO9sFuTvTLTYedMaDbY/YGRSnVg6Ptw9yx4Lg6e2gm3/QkDx5n6MlJg0ySY8R+Y+TTU7WBGh1Wo6pr7+1SAOpeZ768rrf4KPH2hw52uXddZoT3A4qkRViIiIiXMy90FiIiIiIiIiIiIiJSU+67I7tBhsVi4skUt/tp0iFZ1KzGsTR2GtK7N+9e15WRyGh4ezgcrlo/py/Ldx3l26hYAOr4xj49uvIzXpm9n19EkagT60rhGAFtfG5h1zf29GjNuVlTWcz9vD8bOyP7g/v3r2vLU5M28fFULRvdo6HRt4kItR8KcF8wIoVqtIDMVmpex0VXntbkepj8GhzZC3fY5j6UkmtdZ5zLocFfB63S6D9b/YLrlNOqTc8TTiT2mI0lQiMvLL1OCw2HNMsjMAE8HPqaJXQpefqazC0CVBlC/i+lw1PNJx2qIW2Z+ru4cXZUXiwUCa5mvxtldx7DZIHE/nNpnQjne+Y8kdEpIV1j1BaSfc83a507B5l9MF6qK1Qs/vyT4Bpr3Tsxi4L/urkZEROSSoU45IiIiIiIiIiIicska2qY2Ua8PYsajPbm/lwnsXNuhHvf0LFonj7qV/bn+8vpZz48npXHLt6vZdTQJgPziPtMe7k6HBlWIeKwHKenWHMeemrwZgNdn7OCzRdFFqk9cJLAWhPY0I6wiZ5jRRCFd3V2Vc1pcDZ4+plvOxRaNg6R4083Ew7Pgdbx8zDiihD2w+oucxxL2QJVQx4Io5VHNlibAdTLWsetil0JIF/C6YLRe61Gm687R7Y6tFTndjBq7MPhSmlksUDnEdHtxdSAHzAgvazocXO+a9TZOhPRk6Hy/a9ZzlcZ9TPAuOcHdlUh5sXel6bwlIiL5UihHRERERERERERELml+3oWEDIpg838H5Ln/6ovGZp3Xtn5lpj7YjZZ1gnj5qhYANK8ViI9nzr/KfW/OTtcWKs5rPQoSYmD7n9B0cNkNnPhXhqYDzSikzIzs/Ye3wJqvoOPdpkOJPZr0hyYDYcl7cOZo9v4TMVD1Eh9dBaZTDjg2wurscYjfnj266ryWI81Ioq2T7V/LaoWoCAjrVzwBl7IopDNgcc0IK2um+Z1p0B1qtyn6eq7UqDdgg7h/3FyIlAsxS+D7QSaEJiIi+Sqj/3cgIiIiIiIiIiIiUvoF+Xuz7bWBpGVYiTtxlu2HThNStQKdG1Yt9NrRPRpmjamatfUwD/68IcfxlPTMYg0UiZ3Ch8GMJ02XjfAyOrrqvDY3mA4qsYshrL8Jb0Q8Cf5Vod/Ljq01aBx81hkWjIURn5nxQwkxptPJpa56M8AC8ZGmQ5E9zocoGvbKub9iddPtZutU6PsKeNjxb7EProekIxA+3KGyyzX/KhDcwjWhnF1zzJitAW8UfS1Xq9sBfAJhzyL733v7VpmRdGlJkJ4CGf9+5Xh8DjJSwZYJA9+CjoWMuZPyYdW/3dB2z9XPXESkAOqUIyIiIiIiIiIiIlKMAny9qFrRh/YhVbitSwN6Na3hcJima+NqWY+/vLU9ADsOn3ZpneIk/yrQZIAZBdSoj7urKZomA8AvKHuE1cYJcGCtCRf4V3FsrWqNocuDsGnivyGQo5B+1uy/1PlUgKoNHeuUE7vUhClqt8t9rM31kLgP9q+2b63Iv8HDy/y8JVuDbrB/Tc5OUc7YNgUqVIdmQ11Tlyt5eptgXMziws/NSIP5r8H3g2HXbDgeDcnHTScgnwCoXB9qtTYj/FoMh/a3m9Fsc14woSQp307sMe8L74qmY05GmrsrEhEptdQpR0RERERERERERKSUq1zBh0VP9ybTasPXy/xby2s+X0HsuCFYLBY3VycMfR9OHzJhi7LMyxdajICtU8yH6vP/CyHdoO2Nzq13xTOw+VeY9Rz0f9Xs0/gqI7gFHHUwlNOgW97j0ZoNAS9/M8KqQdeC17HZIGqGGYPlX9mxmsu7Bl1h7TdwZLP9o9oulpEKu+ZCq2tK7yi7xn1g1yw4GQdVQvM+Jz4S/rgXjmw1YZuBb4FvYOFrn9pvOmRFPA03/wb671P5teZrE+4b+AbM+A/sX5V7vJ6IiADqlCMiIiIiIiIiIiJSJjSsXpGw4ADqVvbP2rf5QCLbDiYyYWWc2+oSoFJtqOfkh/ilTZsbTEebH4dD6hkTOHL2g3W/StD/v6bbzpJ3zD51yjGCW0DCHjMCqDCJB+FEdP4fePsGQPMhsP1PyEwveK34SDNGLHyY4zWXdyHdzHbvSufXiFkCaWdK9/e3UW+zzatbjtVqRhJ91QtOH4YbJ8HwT+wL5IDpntPnBdg9x3RkulRsnAg/jTDhzEtBSqJ5za2ugdbXgYc3RM93d1UiIqWWQjkiIiIiIiIiIiIiZYiHh4UZj/YAYMRny7nqk2W8PG07Z1PzH7my/ZAJ7+w5lkRyWhFHs0j5FtIVKtWDk7HQ5SGo2aJo67W9Geq0N51ePLwhqL5r6izrgsPBZoXjuwo/N+4fsy2oC0Xr6+BcAuxZVPBakdMBS+kcreRulWpDlYawrwihnMi/wbdS6e4YUr0pBNbJ/V5JPAgTRsDsMaabzkMrobkT75POD5ixVrOeg5RLZMziik8gZhF8298E38q7jT9DWpL5WfsGmi5T0QvcXZWISKmlUI6IiIiIiIiIiIhIGdOidqVc+yK2Hs61b/yyWELHRDD042Vc9cky+r2/hBavzCElPTPHeWdTM3h40gYOnjpXbDVLGeHhAZ3ugerNoNdzrllv8LvmcZVQ8PAs+prlQfC/YSd7PsCPXQr+VaBmq/zPadzPnLN1csFrRU2H+p0gsKb9tV5KGnSDvStMxxhHWTNh50xoMsCMgiutLBbTLSd2Sfbr3DoFvugKB9bBsI/gpl8hINi59T294KqP4MwRWPiGq6ouvY7tgmNR0PFu8x74biDELXN3VcXHmgmrv4T6XaBue7MvrD8c3Wa6K4mISC4K5YiIiIiIiIiIiIiUMR4eFh7u05iBLWvyx0Nm5MqsPEI5Y2fsyPP65dHHsx6npGfS53+LidhymBu+KkKHCCk/evwHHlljxiK5Qv3L4YpnoN3NrlmvPKjW2HQOis/7dzSLzWZCOaE9TcApP14+0GIEREVA2tm8zzkZB0e2lu7RSu4W0tV0HLKng9HF9q2E5BNl4/vbuA+cO2mCOVNGw9TRpoPOA/9AhzudH1l3Xr0O0OleWPM1HFzvkpJLrchpZnvFM3DPPAisBRNGwrap7q2ruOyaDaf2QpcHsveF9TdbjbASEcmTQjkiIiIiIiIiIiIiZdAzA5vz1W0daR9ShRa1K7Fo5zGW7jrGqeQ0flmzj9SM7G44PZtUz3Ftwtk0Tp5N447xa2j+8mziz6QCcODkOXq/t4j0TCe6RIgUpO9L0PNJd1dRenh6mxBEYZ1yTsZB4n77xiG1vg7Sz8LOWXkfj5xhts2vcqjUS0oDE3Jk3wrHr42cAZ6+2QGF0qxhL7OdeC3s+Av6vAR3zTZhMVfp+xIE1ITpT0BmOR6be3dNYgAAIABJREFUGDkd6l0OlepA5RC4ezbU7QhT7jZjrWw2d1foWqu+MCMOm18QPgtuAYG1FcoREcmHQjkiIiIiIiIiIiIiZdzj/ZsAcPv4NbQbO4/n/9hKs5dmAxDo58VPd3dixqM9WPV8Pzw9LOw5dpbLXp/Hkl3Hcq0VdyKZmGP5dNoQEdep2aLwTjmxS83WnlBOSFeoVDf/EVZRM8wIrKoNHavzUlK1kQmS7HUwlGOzme9vWD/XdZgqToE1IaSbeb2j50GvZ8zYKVfyC4LB78CRLaZjTnl0Mg4Ob4bw4dn7KlSF2/40navmvgSznzcjn8qDI9sg7h/TBenC94vFYt77MYvKdwBLRMRJCuWIiIiIiIiIiIiIlHEDW9bK99hrw1tisVhoVTeIWkF+ZFptfLlkT4Hr/bHhAADvz91Jpzf1L99FikVwuOmCk3I6/3Nil5qQSPWmha/n4QGtrjXdKpITch5Liod9q8rGaCV3slhMuGmvg6P8Dm8yP8uy1IXojr/hkbVQt33x3aPF1dBkACx8AxIPFN993OV896mLf6+8/WDU99DlIVj9BUy+E9LPlXh5Lrf6C/CuAO1vz30srD+kJJb/cWUiIk5QKEdERERERERERESkHJjxaI+sx/3Da2Y97t0sOMd5YcHZXRzeHNmKtS/2551rW7PrjcEsfMqMNImOTwLgk4XRxJ9JpfELMwkdE8EDE/Rhm4jLBLcw22NReR+32Uwop+EVJixijzbXgzXDjCS6UFQEYCtboRF3adAdTh+AU/vsvyZyOlg8odng4qvL1Ty97X9fOctigSH/A5sVZj5bvPdyh8i/oVbrvLtPeXjAoHEw8C3z/vhpRO6wXFly9jhsmQxtbzTdgC7WqI/5HdAIKxGRXBTKERERERERERERESkHWtUNIu7tocS9PZSvb+vA2hf7E/f2UKpW9Mlx3pwnssfgNKoeQI1AX264PAQfLw8a1QhgeNs6LIiK57Xp27POy7TaAJi9/QhtXp3D35sPAZCclsFDP69n28HEEniFIuVMcLjZ5jfC6thOOBtv3+iq82q2ghrNzYfnF4qaAVVCoWZLp0q9pDToaraOjLCKnAGh3fMOK1zqqjSA3mNgZ0R2Z5ny4PRh2L8awq8u+LyuD8N138OhDTB+IJzcWzL1udq67yEzFTo/kPdx/8pQ73KInleydYmIlAEK5YiIiIiIiIiIiIiUMx4eFmoE+uZ5zNPDQq+mNQCoEeiT6/iNneoD8P3yuDyvP52SwWO/bCQxOZ3vl8cxc+sRrvpkmWsKF7mUBIWAd0WIj8z7eOxSs3UklGOxQOtRsG8FnNpv9qUkQswSM2KnuDujlAfBLcA3yP5QzrFdcHwnhA8v3rrKsq4PQ3BLmPUspJ5xdzWuEfVvwKiFHT/3liPhtr8g6Sh8dyUkxBZvba6WkQZrv4HG/aBGs/zPC+sPhzZC0rGSq01EpAxQKEdERERERERERETkEvN/N7TjjRGtaFwjINexbo2r06959siriMd6EDl2EOOuaZ3jvLdnR/LenJ1Zz6OOnC6+gvMxafU+1u89WeL3FXEJDw/TLefo9ryPxy2FyiGmw40jWo0y221TzXbXXLCmQ/NhTpd6SfHwhJAusG+lfedHTTfb5kOLr6ayztMbhn0Ipw/BonHursY1Iv+G6k0LDqlcKLQ73D0XUk7D0veKtzZX2/FvoKjLQwWfF9bPbGMWFX9NIiJliEI5IiIiIiIiIiIiIpeYqhV9uLVLAyz5dM14qE8YALd2CaFlnSD8fTy58fL6THmgKzvfGISnh4Vf1uzPcc2gD/8p9rovFHMsiRf+3Mo9P64t0fuKuFRweN6dcqxWiP3HsS4551VtCPU6wdYp5nnUdAioaUbLiH0adIXju+zr+BE5Hep2hEp1ir+usqx+J+h4F6z+Ag5tcnc1RXP2BMQtd7w7UnBzaH87bPktu5NVaWezwarPoVoTaNy34HNrt4MK1SB6fsnUJiJSRiiUIyIiIiIiIiIiIiI5dGhQhT8e6sarw1pm7bNYLHQMrYqvlye+Xtl/tbznrSFZj6dtOlhiNfZ9fwkAJ5PTeXbKZhZFxbNiz/ESu7+ISwS3gOTjucMfR7dCyikIdSKUA9D6OrPGoY2we57p4uKhj4TsFtLNbAvrlnNqv/keh19V/DWVB/3+CxWqw4wnwJrp7mqct3Mm2DLNSDhHdXvUbFd+6tqaisv+NeY93uWBwv8M8fAwI66iF5hgoYiIAArliIiIiIiIiIiIiEge2odUwcsz779CblmnEgBvX9MaTw8LV7czHSIe/3UTmVZbsdd2Iik1x/Pf1x3grh/WcvM3q7nr+zWcPJtW7DWIuERwuNnG78i5P3ap2Tbs6dy6LUeAxRP+fgzSk6G5QiMOqXMZePkVHsqJijBbjQazj39lGDTOhDzWfuvuapwX+bcZLVe7rePXVq4Pra+H9T/C2RIMkp7aBxOvhbkvQ2qS/det+hz8gqDtTfad3+RKEzQ8XMa7IYmIuJBCOSIiIiIiIiIiIiLikAmjO/P6iFaM6lAPgNeGZ3fUmb75ULHf/39zd+V7bNHOY8yLPFrsNYi4RM1/f3cuHmEVu9SMi3F2JFJAMDTqDUe2gG8QhDoZ7rlUefmYcV97VxR8XtQM0+2oeljJ1FUetLrWdFNZ8Dokxbu7GselJELMYjO6Kp8RkIXq8QRkpMDqL11aWr5ilsDXvc37ecXH8FknM3bNVkiI9tR+c17728Gnon33atwXsJhuOSIiAiiUIyIiIiIiIiIiIiIO8vP25LYuDbI66VSu4MOsx82H/k/8tomEYu5Us2n/KQAGtqyZ5/F9J5KL9f4iLlOxBlSoBvHbs/dlppsPzxs6ObrqvNbXmW2zQSZkIo4J6WpCTSmn8z5+9jjsXa4uRI6yWGDwO5BxDha+4e5qHLdrLmSmmVCOs2o0MyPl1nyd//vLFWw2WPEJTBhhxobd/w+Mngf+VeC3W2HSDXAyLv/r134D2KDTffbfs2J1qNMOoucXtXoRkXJDoRwRERERERERERERKbLGNQKyHrd/fR4p6ZnFch+r1UbkYfMh5qc3t+eqNrVpEhyQ45xPF0UzbdNBp++RabUxafU+zqUVz2sQyWKxmE4rF3bKObQJ0pKKHsoJv8p0yOk4umjrXKoadAObFQ6syfv4zlnmeLhGVzmsehMT9NjwExze4u5qHBM5DQJqmU5KRdHjSdN1Z/0PLikrl7SzMHU0zH3JBIDuXWA6OtXvBPctgQFvQtwy+KwL/PM+ZKTlvn79jyZ0VjnEsXuH9Te/N+dOuu71iIiUYQrliIiIiIiIiIiIiEiR+Xh5sPL5vlnPu7+9sFjuM/rHtVmPvT09+PTm9rx/fVtCqlZg0dO9CfT1AuDxXzdx3ZcrSMuwOhwQith6mBf+3Er4K7M5cFJdd6SYBYebUM75UTKxS8y2qCOnfAPhzhkQ0rlo61yq6l0OFk/YuzLv45HTTVihVuuSrau86PWs6dgy+/nCxyiVFmnJsHu+Cbx5FPEj1nodTPBu5WeQkeqa+s5LiIFvr4Rtf0C/V+D6CebPg/M8vaDbI/DIGmjSHxaMhS97mJDOeVt+g5RT0OUhx+8f1t8E1mKWFP21iIiUAwrliIiIiIiIiIiIiIhL1A7yZ+PLVwJw4mwatgs+aJ20eh/bDiZyPMm+Dx8zrTYSz6UDsD8hmbVxCQAs2nkMgPeva5t1bpt6lVn6bB8aVq/Iupf7Z+1fG3eSruMW0Pzl2TlqATMCa8Kqvbnuu+9EMt/+E5P1POrwGbvqFXFacLjpjJO43zyPXQo1W0HFau6t61LnGwC125pRYhdLOQ0xi8wII4ul5GsrD/yrQN8XYe8yiPzb3dXYJ3q+GbtVlNFVF+rxJCQdgU2TXLMemNDQ173h9EG4ZQr0fCr/92hQPbhhItz8u3ldPwyFPx+ApHhY9aV5/4d0cbyGuh3BLwii5xXppYiIlBde7i5ARERERERERERERMqPKhV9sh7fN2E939zekd1Hz/DCn1uz9se9PbTQdT5bFM0H83Yx/ZEeDPvU/Ov9sVe3zDp+Tfu6eV7n6+XJo33D+GRhNGDCQQANn5+Zdc5j/Zrw8YLdAAxvW4cgf28AbDYbV7y3KMd654NBIsUm+N/3dXwkVAyG/auh493urUmMBt1gzTemk4mXb/b+6HmQmWZG+4jz2t8Ja78zI5aaDARvP3dXVLDIv8G/KjTo7pr1GvWGOpfB8o/gsttMBxtn2WxmDNXCN6BmSxO2qdrQvmubDjSduf75Hyz/GHZMg/RkGPmVc6EzTy9o1AeiF5i6FFy7NO1bDRWrQ7XG7q5ExO3UKUdEREREREREREREXGrLqwMAmLfjKLO2HubK/1ua43hSakahayyMigfICuQAvDJtOwD3X9EISwEf8j01oBkf3tAu3+PnAzkACyKPZj0+ejp3F5+nJm8mPdNaaL0iTgtubrZHt8OBtZCRYsbaiPs16AaZqXBwQ879kdNNgKp+J/fUVV54esHAt+DUPlj1mburKVhGKuyaA82HFC08cyGLxXTLORkLO/5yfp3UM/DbrbDwdWh1LYyea38g5zyfCmbU1YPLoW4HqNYEWo50vqaw/nDmMMTvcH4NKbsSD8KPw+C7K+H47sLPFynnFMoREREREREREREREZeq5OfN1e3qAPDgz9kfZn95a3sA1u89WeD1icnpbNp/Kt/jD/UJK7SGEZfVJXbcEBrXqFjgeU/+vpknft0IQPyZFAA+vKEdUa8Pyjqn5zuL8ry2MMlpGQz6cCnjl8U6db1cIvyCoFI90ykndilYPEwYRNwvpKvZ7l2evS89BXbPM+EMD0/31FWeNO4DzYbAPx/AmSPFf78j22DjRLBmOnZdzBJIPQ3hV7u2nuZXQfWmsOxD01XGUQmx8E0/2DnLBJyu/RZ8Cv7vXoFqNIM7Z8Aja3N2h3JUWD+zjZ7v/BpSdi19D2xWwAITRsLpQ+6uSMStFMoREREREREREREREZcbcVnO8VKP9g2jV9NgAO4Yv4bQMRH5Bm+W7D4GwIAWNQny92b+k1fwn/5NAfj9/q5Z46YKY7FYWPBUb7a9NpC6lf35+rYO7HxjEEH+3rw2vCXXdagHwF+bDvHQz+sZ/qn54L1h9Yr4eXvy2c0mRHTkdAo7Dp3mTEo6l42dy+xt9n1wvHHfKaKOnGHsDNMpYPfRM6SkZ2K12jh2JpUtB/IPHsklJjg8O5RT5zIT1BH3q1AVajSHfSuz98UshrQkCB/mtrLKnQFvmE40C14vnvUzM8xIpu+HwpfdYdrDMP1xsDrQBS3yb/CtBI16ubY2Dw/o/gQc3WrCXo44thO+Hwxn4+G2P6Hrw64bFVXUdSrVMaP5FMq59CTEwMYJ0OFOuHUqnDsFE66B5AR3VybiNi7qryYiIiIiIiIiIiIikq1HWPWsx+9f15Zr/w3ABAf6En/GjIka8dlyfr6nM93DqjNt00FOnk3juo71eewX07nmpaEtCKlWAYDH+wfyeP8mTtUS4OvF8jF9s55v/u+ArMcHTp5jZcwJZm7NDtq0rFMJgKFtarM3oRnvzt7JkI//yTr+wMT1THu4O23rVyYj00pKhpVlu4/j7+NJr6Y1sFptTN1wgGembMm6ZuO+k4z8fEWu2laM6Uudyv5OvS4pR4LDIXaJ6SzQ7VF3VyMXatANtkw2nVU8PCFqOvgGQahGjLlMtcbQ5QFY8Sl0uscE01zh7AnY8AOsHQ+nD0DlELhyrAkHLP8QvPxgyHuFB1AyMyAqApoOLFr3mPy0vg4WvQXLPoCmAwo/H+DwZtOBxMML7pwJNVu4vq6iCusHq76A1CTwDXB3NVJSFr8NHt5wxdMQWAtumgQTr4VfboTb/jKj0soamy07YNawF3j5uLceKXMUyhERERERERERERERl/P29GDbawPxsEAFn+y/iu7bPJhf1+7Pen7Lt6tpEhzA7vgkAKpUNB90+Hl7ZAVyitPrI1rS/4OlWc/fHNkKL8/sJvM3Xh7Cu7N35rru6s+Ws+bFfjwzeQvR8UkcPHWuwPtMXLUvz/3R8UkK5QjUbAmZaeZxQ4U9SpWQbrBuPBzZCjVbQdTMf8MZ+lDWpa54Bjb9ArPGwN2zi9ap5fBmWP01bJ0MmanmQ/Qh70LTQSZYZbOBNQNWfgrefnDl6wXfb+9yOJcA4cOdr6kgXj4mjDf7Odi7Ehp0Lfj8/Wtg4ijwDYQ7/jahptIorD+s+Nh0AGs+xN3VSEk4ugO2/A7dHzOBHDD/Tbv2O5h8B0y+E278GTzt63hYKuxZBPNfhcObzHPfIPN+bjHCjN8rjqCelDsaXyUiIiIiIiIiIiIixSLA1ytHIAfgjm6huc47H8gB+HPjQXw8Pdjy34HFXR4AYcGBxL09lAd7mw81r+tQP8fxqhWzP3if+VhPJozulPV85GcrWLLrWIGBnPMdg6ZuOJDn8a0HE52uXcqR4HCz9fCG+l3cW4vkdD4gsW+l+TqXAOFXubem8sgvCPq+BPtXwfY/HL8+Mx22TYXvBsJXV5g1LrsFHlplgivNh5pADpgAzoA34PJ7YMUnprNHQSKng5e/6fxSXNrfDhWqmW45BYlZAj+NgIrVTHiptAZyAEK6gndF94+w2jYVpt4DexY6NrJMHLfoTRMW6/5Ezv0thsPQD2D3HJj2SNn4ORxcDz8OhwkjTHetEV/Azb+bP/93zoRfboD3wuCP+0xYMz3F3RVLKWax2WzuriFLx44dbevWrXN3GSIiIiIiIiIiIiJSzFbsOc7N36zO81jzWoHMfqLku4VkZFpzdMk5L/FcOqfPpVO/qunccyo5jXZj5xW41qKne+Pv7Un1AB/CXpwFQPUAX36463JGfbmCuU/04or3FgEQ9/ZQF78SKXPSz8FbdUwg5+5Z7q5GLvZha6jdDgJrw4Yf4dkY8Kno7qrKH2smfNULUk7BI2vB244uYjYb7JgG816BU3uhSih0ug/a3QL+lQu5nxX+fhQ2TYT+r0KP/+R9zgfhUP9yuGGiEy/KAUveNaGGB5ZBrda5j++aA7/dBlUbwe1/ZXciKc1+uQmOboPHtxSt+5EzbDb4531Y+LoZ82XNgGphJozV7mYTBBNjybtwLApGfg2eTg7aObgevukLvV+A3s/lc5/3YNEb0PURE4wr6feEPY7vNu+ZHdOgQnXTxavjXTk74mSkmZGT2/+CqBnmzyyfQGg2GFpcDY3/HZmakWK64GWkmGtyPE8xo/HqtoeAYPe8Vikyi8Wy3mazdSzsPI2vEhEREREREREREZES161xdZY805urPl7GmdQM7r+iEV8tjQFgf0KyW2rKK5ADEOTvTZB/9qiFyhV8cozcGta2Du3qV+bu7qEAxJ9JpWYlv6zz17zQj/mR8VzTvi5+3p5EvT4YgLDgAKLjk0jNyMTXy7OYXpWUCd7+0PlBCOns7kokLyHdTLcPLz9o3E+BnOLi4QmDxsGPV8GKT6HXMwWff3ADzHnBdDAKbgk3/QpNBoKHnYNCPDxg+MeQcc6Mp/GuAJ3vv+ge6yDpSPGNrrpQp3th+Uew7EMY9V3OY9v/NN1earaC2/6EClWLvx5XCOtnuoqc2APVwwo+N/0crP0Wds6GPs9DaA/n75uZATOfgvU/QOvr4aoPTDeTNV/D7DGw4HVoc735ntds6fx9yoNzJ+GfD8zvQVA9uHKsc+ssfAP8q0KXB/M/54qn4ewxMzquYg3o8UT+55a0xIOw5G3Y+LP5b3Lv56Hrw6bzz8W8fKDJleYr88OcAZ2tvzt2X4unWafdzWbEnsZhlUvqlCMiIiIiIiIiIiIibpOWYeXThbsZ3bMRbV+bC8CjfcN4akAzN1dWsPgzKXR6cwEAMW8NwcPD8X/tvTDqKHf/YP5OXN1yREqx9T/A9MfN4xFfQrub3FpOuffbrRC9AB5dD5Xq5D6eeBAWjIUtv5oP9vu+BJfdlj2eylGZ6TD5TvOB+rCPoMOd2cfmvAirv4Jn95RMZ5W5L8HKz8xrr9rI7Nv4M/z9CNTvDDf/VrY6vCTEwsftYNA70OWBvM/JSIUNP8HS/5kAlF8QpJyGbo9A35cdDymkJsGUu2D3XOjxpFnjwqDWoY2w5lvYNsV0KwnpZsI54cPA0zv/dcurlZ/DnOdN4HDPAtMRKnyYY2vELYMfhpruN90eLfhcqxX+uNd8/4d/Cu1vc752V0hOgGX/ZwJbNit0HA09n4KAGo6vlZkOcf/AgfWm45CXn3n/evqarZffv18+Zmuzws5ZsOU3OHMY/CpD61HQ9mbTQac0dhKSHOztlKNQjoiIiIiIiIiIiIiUCifPpnHibBqNa1TEUgY+iNhx6DTHklLp1dSJD24Aq9VGoxdmArDy+b7UDrJjVIuIlLzju+HTjmYEztO7y06XkrIqIRY+6wQtr4Frvsren3YWln9susnYrND1IRO68KtU9HtmpMKvt5iOSCO/grY3mPFHH7WBGs3hlslFv4c9Th8292x3Cwz7ENZ8AzOfhkZ94Mafy2aXpk86mLFit07NuT8zAzb/YkYnJe4z4Zi+L0GddiactG686YB0zddQq5V99zpzFCZdD0e2wND3oePd+Z+bnAAbJ5ruPKf2QkAtE8hqMsCMD/PycfYVlx02G3x6uRn1dmcEjB8EJ6LhvsVQrbH9a4wfZL6Hj220b+xcRhr8cgPELIYbfobmQ4rwIoogdin8eiuknoa2N0HvMVClQcnXYc2EmEWw6RcTDsxIgerNTAC0zQ15hxOlVFAoR0RERERERERERESklFuy6xh3jF9D05oBzP1PL3eXIyJ5sdng/WYQ3AJu/8vd1Vwa5r9qulfcswDqtDedJBa8ZrpJtBwJ/V9z/Yfn6efg5+tg73IY9T1UbQhfXQHDP4H2t7v2XgWZ/jhsmgRdHoLlH0KzIaYeb7/Cry2NZj1nuk09F2cCG1YrbJsKi8dBwh7z8+37EjTum7MzyK65pkNQcoI53u3RgrshHdsJE0dB8nHz/Wo2yL76rJkmjLXmG4ieZ/Z5+kLttlCv479fl0NQ/fLXuSR2Kfw4LLsD2Kn95j0fWBvumWdfCGz3PPh5FAz9AC4fbf+9U5Pgp+FwdDvc/DuEdAEPb/vHzxVV3DLz+165AYwaDzVblMx9C5OSaEZhbZoE+1eBxQMa9Tbdc1pd43xHMCkWCuWIiIiIiIiIiIiIiJRyNpuNhs+bbjmLn+5NaPUy2AVB5FJweDP4V4HKIe6u5NKQegY+bg8BNc0YmEMbTXhj0Djz4X2x3TcJJl4DB9dDg27mg/uno6FiteK758USYkx3GZsVWo2CkV+W7bFKu+fDz9fCLVMh4xwsegvid5guOH1fNKGj/MIuZ0/AjCcg8m/TSWfkF6brzsX2roBfbjLfp5t/N6N/nHH6MOxfDQfXwYF15n2XkWKOVQw24ZzzQZ26Hcpm56IL/X4HxC6BJ6OyQ1/R8024qc31pmtUQUEkqxW+7mWCJI+sc7y70NkT8P0gOL4re5+HF3j6mJ+lp0/Ox97+0OVh08mqKPauMK+xcn24Y4Zzo6pKwok9sPlX01HK08eMtStvwbAyTqEcEREREREREREREZEyIHRMBADNawUy+4kr3FyNiEgpsWGC6ZRSqS70f9UEVEqii0ZKIvw4HA5vgtCecOeM4r/nxZa8a8Z19Xul7HfGSD8H74SasEVaElQLg97Pm/Fk9vw8bTbTKWnmMyaoNPgdM97rfDhh21T48wHT8eTWKXmHdpyVmW46uRxYa4JaB9aa8U5guuk07AlNB0HTgcUf2Dt3EqIXwO65kJ5sugEVJax15gj8X0vo/AAMfDPnscXvwOK3Cu9+s/0vmHzHvyPfbnSyjqPmZ5hxzny/M9P+/crj8YloOLIVuj5iumV5ejl+v32rTfAusLYZ2RVY07m6S5LVCmcOQVA9d1ciF1EoR0RERERERERERESkDMi02mj8gumWEztuCBb9K2gRERPGiPsH6nYEnwole+/kBJj+GHS4C8L6ley9y6Op98D+NdDrWWhzo3NhilP74M8HYe8yaH4VDPvIjPiZ9zKEdIUbJ0GFqq6v/WLJCSagE7MYds4yI7jAjLdrOtCEdOpdXvQwlc0G8ZGwe44Z5bV/NdgywS/IBMeG/A863ev8+kveg0VvwKMboFrjnMesVph0vemic9dsqNch9/XWTPi8ixmv9OCKkgmPZabD3Jdg9ZfQqI8ZO+XIz3z/WpgwEgKCTSCnUu3iq1UuCQrliIiIiIiIiIiIiIiUEV3HLeBwYgqeHhb2vDXEoWtfmbaNsOAAbu8aWjzF5SEtw8qnC3dz9WV1aVwjoMTuKyIilzCrFVZ9BgvGgoc3pJ+FFiNMp5bz45dK2vFo2DXbfO1bCdYM8K8KTQaYkE7jPuBX2b6xQ2nJJoi2a47piJO43+yv1cas1WSgGc3143A4vhMe2wS+Tvw32JoJH7aB6mFw+7S8z0lOgK96me5E9y/NPcJt0yT460G4fgK0GO54DUWxYQJEPGm6aN30CwSHF37NwfXw0wioUA3umgmV6hR/nVLuKZQjIiIiIiIiIiIiIlJG7D1xll7vLQbgj4e60T6kil3XfbM0hjdnRgIw6Z7OdAurXlwl5vDTyjhembYdgN/v70qnhiXQnUBERATMSKmZz0D9TtD3lZIZa2aPc6dgz4LsUM25k9nHPLzB08eMnPLyzX58fmvxhGNRkJEC3hVNmKfJAPN1cUeXA+vg237Q+wXo/ZzjdUbNhF9vKjxQc2gjfDcAQnvALVOyu+FkpMGnHUz46L7F9gWOXG3/Gvj1FjPK65pvoHkBgeZDG+Gnq0046q6ZGgMlLqNQjoiIiIiIiIiIiIhIGfLclC38ts78q/jtrw2k5X/n0LZeENMe6ZHvNaFjInI8//LW9jwwcQND29Tm/evasigqngd/3gBA3NtDXVbrxfc7phntAAAgAElEQVR15doAG/ed5NXpOxh/R0eqBfi6dG0REZFiZ82EA2th7woTtMlMM+OXMtPyf1ytCTQdAA26m+BOQX67DfYsNN1yAmo4VtvEUXB0GzyxrfBRYut/gOmPQ6/noM8LZt+ab2Dm03DrVAjr79i9XSnxIPx2iwnd9HkJrng6d0Do8GbTWcivkhlZVTnEPbVKuWRvKMeJgX0iIiIiIiIiIiIiIuJqb13TOiuU0/K/cwDYfCDRoTUemGgCOBFbDhOx5XCOY8fOpFIj0DUBlz7NarBo57Gs52dS0gn083bJ2jabjZGfrwDgqcmb+eGuTi5ZV0REpMR4eEJIF/NVHPq9AlERsPQ9GPKu/dedjIPo+SZkU1ggB6D9HbB/LSx5B+p2NF1zlr4HId2gcT+ny3eJoLpw1ywTGlr0BhzdCld/nj3S68hW0yHHNxDumKFAjrhNKenlJSIiIiIiIiIiIiJyafP0sLDzjUG59p9ISs3z/AMnk/H2tHB/r0ZseuXKQte//M35JCanF7lOgGNJqfRqWoMvb+0AQOtX5/LwzxtIz7QWad0nf9tEw+dnZj3fvP8UmdbS0/FfRESkVKjeBNrfDuvGQ0KM/det+x4sHtDhDvvOt1hg6P+gZmv4416Y/19IOgr9XnbP2KqLefvDyK9gwJsQOR3GDzTBo6M7TCDHuwLc8TdUaeDuSuUSplCOiIiIiIiIiIiIiEgp4evlyavDWgDQsHpFAEZ8vjzHOdsOJtLildn0eGcR6Zk2GlarSOUKPtzZLRSAdS/1J+r1Qbw1sjUAY69umXVt27Fzue+ndYSOiWDO9iOF1mO12nKFYpLTMth28DQNq1eka+NqWfsjth5m0up9Oc612WyEjokgdEwECWfTCr3XHxsPZj2/vmM9Tians3hnfKF1Ttt0kHGzIjl06hwr95wo9HwREZEyr/cY8PSGhW/ad35GKmycAM0GQ6U69t/H2x9u+AlsNljztRlZ1aCbczUXB4sFuj0Ct0yBxP3wdR/4cRh4+sAd06FqI3dXKJc4hXJEREREREREREREREqRO7s35P/Zu+/oqqqEjcPvSe8hIUBCTQgldIHQe28qir13BGGU0c8RsCGiYBkdHRWxMoqCDQuGjvQeOoQWIPQeEtLLvef7I+GSazpckgC/Zy2W5+x29olrbW7Im70PTByoWf/oLEk6HJ+m7u8s1ubDCZq/44Ru/O8KpWZabO37NK4mSXr1psba/+ZABfm4y8PVWfe2q61dr/fXgx1CtW1cX1v7+TEnJUlPfrtB+08n2z37VFK67dpqNVV37GyFj52t7UdzjtHadzpZ//p5qyQpvKqP/D1ddWvLGrY+r/6xwy7EszXP8VvT19kHdv7uyLk0SVLTGn6KfWOA/tGzviTpsf9FS5KyLFbd/dlqLd972q5fZrZVz8zYrClL92vQh8t1z+drlJbn6wMAwDXJN1hq/5S0/Wfp2Obi28f8IaWelSIfLf2zAutKQz6T/GtLvV4tff+yUK+X9MRiybuK5OSSc2RV5fDynhUgwzSvzLaPhmF8JelGSadM02xakj6RkZFmdHT0FZkPAAAAAAAAAABXm1EzNum3zccKrZ/+RHu73WqK8vvmo3pmRv4f2h2YOFBL95xWUnq2/jF9k57t00BP96qvKUv3aeKcXbZ2rWpX0sZDCbb7aY+1U+f6QZKkWVuO6fmftyg96+LxVTUqeSolM1sJeY7M2jm+vzzdnO2ev+VwglycDSWnZ+uuz9bYxk3Psiji5bmSpLhJg/TQV+u0dE9OIKd5TX+9fXtzRQT7Ke5Mirq/u8RuzBE9wnV3m9qqGeApoyIcrwEAwJWQnih9cIMU0kJ68Lei237VX0o6If1jo+R0iXt3mGbFOLaqKJYsyZIpuXmX90xwjTMMY4NpmpHFtruCoZyukpIlfUMoBwAAAAAAAACA0jNNUy/8slU/Rh+xK//0/laqFeilJtX9Sz3emJnb1KV+FY34fqMkaVi3cH26dJ9du7hJgxQ6OqrIsfa/OVBOThd/MLftSKJu+mhFvnbebs5qGxaoxbtzAjU3taiuiGBfNQrxVYCXm279ZJVd+/n/7KoG1XwlSRPn7NSUpfs1pGUNu6OtLpg7qovOp2XrzimrC5xjvao+WvhstyLfAwCAq9rqT6R5Y6QHfpXCexbc5uQOaXJHqc/rUqeny3Z+wDWqpKGcK3Z8lWmayyTFX6nxAQAAAAAAAAC41hmGobdvbyFPV/vdZfo3DSl1IOfCeJNua65BzUP0+uAmkpQvkCOpyEBO85r++mlYB7tAjiQ1q+mvJ7qE5Wv/4qDGeqZ3A9v9rC3H9M683Xp0anS+QI4khVfxsV0/3DFUkmyBnAc71FHzmhffOzrunP7adUqS5OvuIkmqW+Xib8bHnrI/nqsoq/edVejoKL05e2eJ+wAAUO7aPJZzrNTCcZLVWnCb6K8lZ3ep5f1lOjUAkkt5TwAAAAAAAAAAABRt6fPdlZJpUTU/dzlqA/w7Imvp5d932JUF+bjrTHKG7f6NW5uqW4Mqij2VrJoBngqt7C0X58J/3/eF/hHq2yRYEcG+av36QmVarLqpRYh83F30YIc6ik/J1J9bjxfaf93YXnLOE/YJ8fe0Xd/brrbGD87ZmD8jO+doq29Wx2nPyZzgzbbX+slqNWUYUtiY2ZIkdxcnbTgYr/Vx53Rfu9ry9XAt9Nn3fL5GkvTZsv3afzpZXzzUptC2AABUGC7uUs+XpF+HSjtmSs1ut6/PSJa2zJCa3Cp5BZbPHIHr2BU7vkqSDMMIlfRnUcdXGYYxVNJQSapdu3brgwcPXrH5AAAAAAAAAACAi9KzLOowcZG+eKiNTNNURIifVuw9rWHTco622vvGALkWEcIpimmaMgwjX3nU1uPy9XDRugPxahsWqNZ1AhR3NkXZFlMtalUqcByL1cwXBhr80QptOZIoSepcL0jTHm9nq7NaTX26bJ/enrvbVubn4aKt4/rlG3/mxiOqU9lLt022PwJr7dhequbnYVe2dM9p1Q70UliQtwAAqDCsVmlKVykzSRqxXnJxu1gX/bX05yjpsQVSrbblN0fgGlPS46vKPZSTV2RkpBkdHX3F5gMAAAAAAAAAAK4NH/21V+/O3yNJ2vBSb1X2cberX3cgXndOsQ/azBvVVb4eLur+7hIN7VJXHy2Otat/89ZmSs3M1oSonaoV6Knl/+ppq0vOyFbTV+cpIthXc0d1veR5bzmcoAUxJ/Vc3wYFhpYAALgkexdK390mDXhHajc0p8w0pSldJFPSsOUSf+8ADlPSUA7HVwEAAAAAAAAAgKvO8O71FF7FR32bBNsdeXVBZJ2AfGX9/rPMdv33QI4k3dqyhjxcnTQhaqcOx6fpXEqmArxzdhto+uo8SdKuE0mXNe/BH6+UlHNM1p43BlzWWAAA2NTrJYV2kZa+Jd1wj+TuKx2Jlk5sk258n0AOUE4ubc/JEjAMY7qk1ZIaGoZxxDCMx67UswAAAAAAAAAAwPXF2cnQgGYhBQZyJMnJydCMoe31y/COips0qNBxalTylCSN7FFPnm7OMgxDAV6ukqSWry9Q6OgoLdp50q7P9qOJlz3/TItVMcfOK/ZU8mWPVRZijp1X6Ogo7T15eaEkAMAVYhhSn9ek1DPSqo9yyqK/lNx8pGZ3lO/cgOvYFT2+qrQ4vgoAAAAAAAAAAFwJ51Iyddunq7T/dIqe6h6uXo2qqWkNP7m7OOdrm5iapRbj5+crjwj2te2UE/vGAG05kihvd2dFBPsV+Myk9Cx9s/qgKnu7qX3dyjqemK57Pl+Tr93mV/qokpfbZb7hldXgpTnKzLZKkp7uWU83tqiuBtV8y3lWAIB8fnxI2rtAeuIvaUpXqeX90o3vlfesgGtOSY+vIpQDAAAAAAAAAADwNytjz+i+L9balU19pI0e/np9vrb3t6+tCbc0s91PX3dIf+06pdqBXvpyxYF87V+5sbHG/xljux8zIEJPdgt34Owd758/bNavm47alRW1AxEAoJyc3Sd91EbyqSolHZeGr5KqNSnvWQHXnJKGcq7Y8VUAAAAAAAAAAABXq071ghQ3aZDiJg3SoOYhCvR2U/eGVTVmQES+ttPWHFJapkWmaar/f5ZpzMxtWhBzssBAjiQ90KGObm1ZQ70bVZMkTZyzS3sKORYqPcuixNQsx73YJbJYTduxXnl9sXy/Pl+2X6Zp6j8L9yh0dJQmL9lXDjMEAEiSKodLrR/OCeTUak8gByhn7JQDAAAAAAAAAABQCnd+ulrr4uL1SKdQfb0yrtj2425qrElzdyk9y6qBzYL1yX2tbXVt31ioU0kZkqQF/+yq+rlHQs1Yd0hTV8XJ3dVZWw4nKGZ8P3m5uVyR9ymOaZoKGzNbkrT0+e66bfIqxadkauu4fmr66jxJ0oge4fp48cUwzs7x/eXplv9oMABAGUg6KX3VT+o/UWo4oLxnA1yT2CkHAAAAAAAAAADgCvj8wUgteq6bXrmxsRY+263ANpW93XR/+9paO7aXHu4UplG9GyjQ202j+zeyazdvVFfb9eQl+xR3JkWPTV2v0TO3adeJJG05nCBJmrnR/uiosjTgg+WSpBa1KqlOZW+9PriprKa0aOdJW5u8gRxJavTK3DKdIwAgD99q0jObCeQAFUD5RKoBAAAAAAAAAACuUv5ervLPPcqpXlUfjRkQodX7z+qGWpVUydNVD3cKy9dnWLdwDesWnq88wNtNresEaMPBc1q657Rmbio4fLPlcILub1/HsS9SAvN3nNCuEzlHa711WzNJUqMQP0nSMzM252s/7bF2uv/LtWU3QQAAgAqMnXIAAAAAAAAAAAAuw5PdwjX1kbYa1btBgYGc4vz4ZAcNvqG6zqZk2pX7ul/83eqfNhxRepblsudaWv+ev8d23aBqztFatQO97Nr0blTNdt25fpBeubGxJOlEYnqRY588n67Q0VGau/24o6YLAABQoRDKAQAAAAAAAAAAKEfOToYGNA2xK1s7tpc2v9pXPw/roDEDIiRJMcfPS5IenbpeoaOjig29OEKAt6sCvd10YOJAOTkZkiQnJ0PP92soSZpwS1N98VCkXh/cRHOe6SJJal+3siQVG7aZvCTnyKth0zaq48RFV+oVAAAAyg2hHAAAAAAAAAAAgHLWv2mwtr/WT3NHddGu1/urmp+HnJ0MRYYGqkdEVUnSkE9W6XB8qv7adUqS9O2auBKNPeK7jer7/lJlW6ySpD0nk2SaZqHtk9KzFB0Xr+1HE7Vmf7we6xwmwzDsx+xRTzvH99d97WpLkh7oEGo71qpRSM6OOuNmxehwfKqGT9ugzm/9pVWxZ/Tcj1uUlTuPI+fSbOMdS0zXy79tL9H7AAAAXC2Moj50lbXIyEgzOjq6vKcBAAAAAAAAAABQYWRbrKr34pwC6+aN6qqGwb4F1g34YLl25u6uc8HCZ7uq93vL9FjnML2ce8zU3/V8d4n2n0mx3a8b20tV/TxKNef/Ltqrfy/YU2Dd8/0aqk/jaur7/jJJ0qOdwvTVygO2+vfubKEhrWqW6nkAAABlyTCMDaZpRhbXjp1yAAAAAAAAAAAAKjAXZye9fVvzAus+XhxbYPnuE0n5AjmS1Pu9nCDMlysOKD3LUmDfvIEcSaUO5EjS8O7hhda9M2+3LZATWSdAYwZG2NU/++MWbTp0rtTPBAAAqGgI5QAAAAAAAAAAAFRwN99Q3e7+u8fbSZKcjIJaS2NmbrW7X/Rct3xtFuceg5XXhRMWIusE6Psn2in2jQGXMl25ONv/COrCMVd/99OwDnJ1dlLcpEF25bd+skpHE9IK7AMAAHC1IJQDAAAAAAAAAABQwXm4OuuPkZ0U5OOuFS/0UKd6QepUr7J+23xMZ5Iz7NparaYt0DKoeYj+eq6bwqv4qKqvu6Sc46Mkafh3G5WRbbH1ScnIVtiY2ZKkGgGe6hgelC9cUxo/PtlB1f09tPHlPnrj1maKmzRI3+eGiSTpj5GdZBgXU0VfPBgpPw8X232nSX9d8rOvVpnZVi3aeVLrDsSX91QAAIADGBcSzxVBZGSkGR0dXd7TAAAAAAAAAAAAqPDGzNyq6esOS5Jeu7mJHuoYKknadzpZvf69VBOHNNM9bS/uULPz+Hkt23NaT3YLV+joKFv56jE9NfSbDdp2NNFWtm5sr0s6tqo4FqupKcv26Y7WtVQlNyT0dz3fXWI7QsvbzVkPdQzVv/pHFNj2Upw6n64DZ1L0/sI9WrM/XsO7h+sFB45/OT5ZEqu35+6WJO0c31+ebs7lPCMAAFAQwzA2mKYZWVw7dsoBAAAAAAAAAAC4Cv2r38Ugyat/7FBcbpDl6LmcXXLCq/jYtW8U4qcnu4VLkj69v5WtvO97y+wCOV89HHlFAjmS5Oxk6Knu9QoN5EjS7Ge62K5TMi36ZMk+OeqXzLMtVrV9c5Hu+myN1uzP2Y1msgPHvxTj/tihaWsOSpLmbT9hK5+8JLa8pgQAAByEUA4AAAAAAAAAAMBVKMDbTb+P6GS7n77+kObtOKEthxMkSbUDvQrt279piD64+wZJUlJGtq183E2N1TOi2hWaccl4uDrrh6Ht7cqOJ6aXuH/MsfNKyX2nPSeTdC4l01Y3c+PRAvuEjZmtA7mhpgsW7z6lw/GpJX7updh06JymrorTS79tV+joKG05kqgu9YMkSR/+FVuuYSEAAHD5XIpvAgAAAAAAAAAAgIqoRa1Kips0SPd8tkZfLD8gi3W/ra6aX+G70UjSzS2qa9OhBE1dFSdJWvRct3y765SXdnUr69enOuqDRXu1ZPdpLYg5aTueqzDnUjI1btYO/b75mF154xA/2+47E6Ji7Or+GNlJN3+0UpLU490l2vV6f7m7OOnTpfv11txdtnabXu6jAG+3Ap+bnmXRhKgYjerdQEE+RX/N87JaTY38flO+8kc7hWntgXhlZlu149h5Na3hX+IxAQBAxcJOOQAAAAAAAAAAAFe5zvWDZLFe3FUlIthXhmEU2ccwDN3XrrbtvqgjpcpDy9oBeuf2FpJyjucqzvg/Y/IFciQp5vh5SdL/VsXpfPrFXYG+f6KdmtesZLfbUMTLcxU2ZrZdIEeSxszcVuhz7/h0taatOaSxM7fZ/T8ozpDJq3Q0IeeosVa1K0mSvnu8nXpEVLUdL1aaHYIAAEDFQygHAAAAAAAAAADgKjekVQ3bdd0q3op6ukuJ+tWt4qNAbze1qOkvX/eKd8BCkM/F3WmKCrzEp2Tq100FH00lSbtPJNmCPQ93DFXcpEHqGJ5zTFSLWpX0y/AOBfZ7sltdSdLcHSeUnmXJV2+1mtp2NFGSND/mpJqNm1fMG+XIsli1OfeYsYlDmumX4R11YOJAdaqXM6fGITm74zzxTbSS0rNKNCYAAKh4COUAAAAAAAAAAABc5UL8PTXnmS7aNq6v/nquu5ydit4l5wJnJ0MbX+6j30d2LnZnnfJgGIae79dQkjR3+wkt3n1KzV6dZxdUGfHdRrV6fYHtvm1YoKY/0V5rxvTSyzc2liT1+88yW/0TXevme07rOoGa+VRHdW1QRVLOsVYrXuihMQMa2dq0mbAwX78GL82xu0/NtOhwfGqx77Uy9owkaUjLGrqnbW0ZhmH39c+7a1GzcfNLtQMPAACoOAzTrDh/iUdGRprR0dHlPQ0AAAAAAAAAAABUEGmZFjV6Za4GNgvW7G0nJEnv3tFCt7euqU+WxOrtubttbbeN6ytfD1fbfVJ6lpqNm2+7X/hsV9Wr6luq5+88fl4DPlguSVo9pqdC/D1ltZrafyZZvd/LCfusG9tLX644oCnL9tv1nfpIG+06kaSvVx7QrJGd5efpqtdmxcjPw0VTlu3XqtE9Vb2SZ4HPXbzrlB6Zut6ubOu4vvLL834AAKB8GIaxwTTNyOLaVbx9CAEAAAAAAAAAAIBcnm7O6hlR1RbIkaStRxL03dqD2nQo5wioN29tprvb1JLT33YI8vVwVed6QVqRuzNNjUpepX5+w2q+6lI/SMv3nlGHiX/lq//6kTaq6uehF/pH5AvlPPz1xVDN/JiTOp2UoenrDtnKgnzcVZgeEVV1T9vadu2fmrZR0x5vl6+taZpKSM1SgLdbvrq8Nh9OUESwrzxcnYtsd7lM06yQOy8BAFDWOL4KAAAAAAAAAAAAFZqvh/3vmX+z+mIgR5KGtKqRL5BzwbTH2+n7J9pp7MAIebqVPozi5GRo8v2tC63v0bCqrd2+NwdqYLPgAtst3nVKO4+ft91X83OXm0vRP6obd3NjfXRvS7k657ybi7OhYwlpCh0dpdDRUbprympJ0oeLYtXy9QWauvJAvjGSM7KVlJ6l2FPJuuXjlZoQFVP0C1+ihi/NUejoKE1deUBhY2YrPctyRZ4DAMDVhOOrAAAAAAAAAAAAUKHFHDuvgR/mHCFVo5Knjiak2ep+HtZBkaGBV3wOP284ov/7aYtd2cMdQzXu5ib52mZkW5SUnq3ICQtVM8BTYUHeWr73jF2b6v4eWjWmV4mf//T0Tfpz6zHVDPDSofhUW/nvIzpp8Mcrbfd5j8TadOicbv1kldycndQ2LNC2Y9CBiQMdupNNYlqWWrw2364swMtVm17p67BnAABQkZT0+CpCOQAAAAAAAAAAALgqZGRbdOhsqvq8v0yStGZMLwX7e5TJsy/8TM0wDGVkW5RlMeXj7lJMrxzr4+J1x6er7cr+c9cNuqVljRI/f/QvWzVj/eFi2/VrUk1THoiUaZoKGzO7wDYRwb7adSJJPwxtr/CqPgr0cit0p6HiHDybom7vLCmwbv4/u6pBNd9LGhcAgIqspKEcjq8CAAAAAAAAAADAVcHdxVn1q/lqx2v9tHtC/zIL5Eg5YZwLu8u4uziXOJAjSW1CA7Xp5T7q2qCKZgxtr7hJg0oVyJGkvk2q2a4f6lBHS5/vblf/9u3NJUnzdpxUtsVq2xUnr14ROUdt7TqRJEm667M1ipywUHXHFhzeKY5pmrr387WF1o+asfmSxgUA4FpBKAcAAAAAAAAAAABXFW93F7m7OJf3NEolwNtN3zzaVu3rVr6k/j0jqmnCLU3VtUEVvTa4qepU9tbi/+suSWpZu5LujKylPo1zgjtd316sB75cJ0maN6qrekVUVfOa/vrswcJ/oT/m2PlSz+lYYrrtKLHfRnRSFV93SVLV3P/GHD+v12btkGmaslqLPr3jWEKaMrOtpZ4DAAAVGaEcAAAAAAAAAAAA4Cpwf/s6+ubRtrb7sCBvbXq5j358soMk6Y1bmkrKCctc0DDYV1MeaK2ZwzvK2cnQgKbBkqQ3b20mw5B+GZ7T99s1B3U0IU3vLdijuz9breOJacXO59T5nOd8+VCkbqhVSetf7K3RAyL007AO+vaxnHl+vTJOt3y8UnXHzpbVWnA4Jz4lUx0n/aVxs3ZcypelQpi/44QW7z5lO+ZMkrItVmVZCBoBwPXMyPsXQ3mLjIw0o6Ojy3saAAAAAAAAAAAAwFUpdHSU7bpFTX/9PrJzsX36/2eZ7UirvOImDSqy3/R1hzRm5jbNHdVFEcF++eq/W3tQL/66vcC+If4eWjW6p04nZei5n7Zo+d6c47b2vzlQTk5GsXOuSJ6ZsUm/bz5mu183tpfembdbP204Ikn636Nt1a1BlfKaHgDgCjAMY4NpmoVvQZeLnXIAAAAAAAAAAACAa8Sf/+isO1rX1NiBEZpwS7MS9elUL6jA8llbjhVYfsHCmJOq7u+hhtV8C6y/t21tRQQXXHc8MV2fLNmntm8usgVyJCni5bmKPZWkhNTMEs29vGVmW+0COZLU9s1FtkCOJD301bqynhYAoIJgpxwAAAAAAAAAAADgOmaaplbvPytnw9Dp5AytjD2r6esOSSp4t5wsi1V931+mA2dS5Ovhom3j+hU6tsVqavysHbqxRXUN+3aDKnm5KrSytxbtOlXsvA5MHCjDqBi75pxITJeHq5MqebnZlW8+nKBbPl6pjuGV1b5uZb23YE+B/SPrBOjn4R3LYqoAgDJQ0p1yXMpiMgAAAAAAAAAAAAAqJsMw1DH84m453RtWtYVyTp5PVzU/D7v29V+cY7t+aVCjIsd2djL02uCmkqT1L/a2HU019JtozY85KS83Z60d20u+Hq6K2npcI77faOv7yu875Ovhopa1A9SncTWZpimrmTOmlBMmOnIuTbUCvUr1vlkWq84kZyjE37NE7X+KPqznf96qsCBvLf6/7nZ1e07mHPs14ZamqlvFR/tOJ9t2zombNEhztx/XsGkbFX3wnD5eHKsRPeqVaq4AgKsbO+UAAAAAAAAAAAAAsLNk9yk9/PV6SdLuCf3l7uIsKee4pgYv5YRynugSphcHNb7kZ2w9kqCIYD+5uTjZyixWU7tOnNegD1fYtR3ePVyTl+yz3X98byu5OBt68tsNalbDX7P+0TmnfHGsWtSspGY1/XUsIU2NQvzyPfe9+bv14V+xWvDPrqr/t6O3Tp5PVxUfd1t4yGo1VXfsbFt9zPh+8nK7uO9B6OgoSVLsGwPk4pzzHofjU1XZx01ebi7KzLaqz/tLdfBsqiTJ3cVJDar56scnO8jTzbn0XzQAQIVQ0p1yCOUAAAAAAAAAAAAAsGOapsLGXAyjPNElTI1C/PTsj1skSfe3r60JtzS7Ys9/9ofNmrnpaInb39++tvo0DtZDX62TJLk6G8qymJo1srOa1fS3a9vwpTnKyLZKsj8ia2XsGd33xVq1qOmv30d21uH4VA39doN2Hj9v6+vp6qydr/eXJGVkW9TwpbmSCj7m6wKL1dSAD5Zpz8lkW1nPiKr66uE2JX4/AEDFUtJQjlNxDQAAAAAAAAAAAABcXwzD0IaXetvuP19+wBbIkaSHO4Zd0ec/2DFUktQ2LFC33FC92PbT1hyyBXIkKcuSszHBTR+tkMWac52eZdHc7cdtgRxJWnsgXgmpmftGRtkAACAASURBVJKk+75YK0naciRRRxPS1OXtxbZAzqyROTvxpGVZdGHTg+i4c5KkcTcVvVuQs5Oh+f/sZle2Yu8Z7T+drOi4+GLfDQBw9WKnHAAAAAAAAAAAAAAFevCrdVq253S+8qJ2hnGU5IxsuTobcndxltVqKttq6nhimir7uKvPe0t1PDFdI3qEq0v9Krr7szWSpOY1/bX1SKIkqVGIny1U8/ZtzXU6OUPvzNstSYoI9tWuE0m2Z93brra+X3uo0LkcmDhQ3609pJd+256vbt3YXqrq51Gid7JaTc3aekzPzNhsK/v+8Xb6ZvVBvXpzY4X4e5ZoHABA+eL4KgAAAAAAAAAAAACXJSk9S2eTM9X93SWqV9VH/ZsEa2TPevJwdS7vqck0TdvRUyO/36g/tx7XVw9HqmN4kLIsVnm6Oqvei3MK7Pv+XS20et9Z/Rh9xK78qe7h+mTJPtt953pB+vedLVTNz0NpmRY1emVuvrHyHoFVEoWNc1dkLb11e/MSj1Ma6VkW/bn1uG5sHlIh/t8BwNWOUA4AAAAAAAAAAAAAhzgcn6pgfw+5OjuV91QKZLWayrRY8wVOElOz1GL8/HztfxneQa1qB2jmxqOavHSfYk8lS5K2vNJXaw+c1dBvN0iS9r4xwO6dUzOzNXvbCS3dc1qzthzTr091VMvaAaWe7/hZMfph/SGlZFpsZe4uTto2rp/cXC7/a2yapr5fd0g9GlZV9UqeCh0dJUnqUj9I3z7W7rLHB4DrHaEcAAAAAAAAAAAAANe9jxfH2o6t+mFoe4VV8VZV34vHTR04k6Jh327QuJubqEN4ZUk5O8ucSExXaJD3FZ3b+rh43fHparuy5/o0ULOa/uresOolj/vegj36cNFeSVL/JsGau+OEra593UAN715P3RpUueTxAeB6RygHAAAAAAAAAAAAACSdTc5QZR/38p5Ggf7YckynzqdrQtROu/JRvevrl41HNLBpiKYs26+aAZ6a/kR71Qr0kiSdOp+ur1fFaXj3cH2yOGe3n4/ubSmL1VTPfy/RyfMZduNFBPtq14kku7LSHr1VEulZFrm7ODl8XACoSAjlAAAAAAAAAAAAAMBVIj4lU61eX3BZY3SuF6QVsWckSUE+7jqTfDGYM2tkZ9300Qq79j0aVtHBs6mq6ueu6U+01/sL98rPw0WPd6lbqueeT8+Sj5uLTpxPV8dJf+m5Pg30j171L+tdAKAiI5QDAAAAAAAAAAAAAFeR44lpSsu06IVftmp93LnLGuvrh9uoYbCvfD1c5OHqLFdnJ1vdzuPnNeCD5Xbtb25RXX9sOSZJquztprMpmdo5vr883ZyLfM6qfWd07+dr9USXMH2+/ICtPG7SoMuaPwBUZCUN5biUxWQAAAAAAAAAAAAAAEUL8feUJP00rKMkad2BeI39dZu+friNJs3dpaitxwvs5+psqJqfh46cS7OVdaxXWe4uBQdqGoX45Su7EMiRpLMpmZKkCVEx+m7tIQ1pWUMzNx21az+qd32N6t1A36w6KEl2gRwpZ+efQG+3It8XAK517JQDAAAAAAAAAAAAAFeRzGyrGrw0R2/c2lS3t64p05Q8XIve0ebvJi/Zp1lbjum7x9up5SUem/XtY231wJfrCq3fPaF/ocEgALialXSnHKfiGgAAAAAAAAAAAAAAKg43FyfFTRqk+9rVkbuLc6kDOZI0vHu4Zj/TRQHebmobFihJ+vT+Vnqqe3ihfdqFBWr84CaqUSlnR5+/B3Ke7llPeyYMsN33fX9ZoWOlZVqUkpFdYN3ek0n6cNFeFbfBRLbFqnMpmUrPshTZDgDKCzvlAAAAAAAAAAAAAAAk5QRdktKzteVIgh7+er3+fUcL3da6pl2b5IxsNX11nu1+zZheOhSfagv3/BR9WM//vFWS9PotTXVzi+ry83CRYRiSJNM0NWTyKm06lKDGIX6KOX7edkSWm4uTMrOtki4ekVWQC7sFSVJoZS8teb5Hoe906ny6kjOyVbeKT4H178zbpZQMi165sbGcnIySfJkAXOdKulMOoRwAAAAAAAAAAAAAQD7pWZZCd+EJHR0lSWpZu5J+fapTvvple07rwa8u7qTTtUEVHTybIldnJ/m4u2jz4YRin98oxE9T7m8tf09X+Xu52tVtOHhOt01eZbtf8UIP1QzwyjdGdFy8bv90te2+ur+HVo3pJSknrLN87xk999MWW/3w7uF6oX+ETNPUH1uO6bkftyjQ203v3tFCXeoH2YJFAK5vhHIAAAAAAAAAAAAAAFfE8cQ03fPZGn10bys1reFfYJunp2/SH1uOFTpGjUqeOpqQZlfWu1FVtQkN1OFzqZq25pCt/OGOoRp3cxPb/VPfbdDsbSf06f2tNWzaBklSm9AAtawdoM+W7ddnD7RWZGigWr2+IN9zfx7WQcv2ntGHi/aW6p2r+Lor6unOavvGIknS5lf6qJKXW6nGAHBtIJQDAAAAAAAAAAAAAChXCamZenTqem08ZL8zztRH2qh93crq/NZinUnO0Du3N1e3BlVU1c9DknQ6KUNt3lho1yfq6c7y83BVVT93NXxprnw9XLTllb6qO3Z2sfP48ckOunPK6gLrIoJ99XSv+hr76zYlpGYVOY6rs6EsS87P2OtV9dHCZ7sV+2wA1x5COQAAAAAAAAAAAACAcme1mrbgzLoXe6mKj3uJjoGyWE19tmy/vlt7UEfOpeWr//cdLXRb65pKSs/SvZ+v1bajiQWO8+4dLXR765racSxRgz5cYSsf0qqGZm48qt9HdFKLWpUkSTuPn9eAD5ZLkiYOaaYhrWroka/Xa9W+swWOHfvGALk4OxX6DtFx8fpkyT59dG9Lebm5FPvOF2RZrHItYlwA5YtQDgAAAAAAAAAAAACgQsi2WGUxTbm7OF9S//GzYvTVygN2ZfveHChnp/zhHtM0tetEkiKCffOFfzYcjNdtk3N2zNn7xoACgy/RcfGq7OOusCBvu/KXftumaWsO6anu4Vq9/6w2HUrQba1q6t93tpDVaup8epa83Fz066YjurlFDcWeStZNH+WEgKY80Fr9mgQrdHSUJGnRc90UXsWnwHf9c+sxjfx+k14a1EiPd6lrK5+x7pBa1g5Qw2Df4r5cAK4wQjkAAAAAAAAAAAAAgGvOgpiTCq/irbqFhFqKk5FtkWlKHq6XFhCS7I/X+mFoe/3rl606eDZVb93WTC/8sk3ebs5KybTY2kcE+2ra4+0UOSGnT6+IqnJzcdKc7SfULixQdat467ZWNbVo1ylNXrLP1u/AxIGasf6wDEmjZ26zlRW201BiWpZe/HWbejSsqtta17zk9wNQNEI5AAAAAAAAAAAAAABcIb3fW6rYU8lFtvF1d1FSRrZDnzuoeYg+vreVJGn3iSTVqewlD1dnJaZlqcVr823t8u608+Kv27Qy9ox+H9lZ/p6uDp0PcD0qaSiHQ+gAAAAAAAAAAAAAACilWSM7F9tm9dhe+uph+5/b39uudpF9bmweormjuhRaH7X1uB75ep2i4+LV7z/LNP7PGK2KPWMXyJGkCVE7FTo6SgtiTuq7tYcUdzZVN/53ebFzBuA47JQDAAAAAAAAAAAAAMAlOJucoX2nU/TN6jjVqeyl3SeSNeWB1vpjy1Edjk/T073qK8tiVf0X50iSlv+rh5ydDHWc9JcG31BdH9zdUot2ntSsLcf02+ZjGj+4iR7sECpJmjRnlz5duk+Lnuum8Nyjuj5eHKt35u0uck67Xu+viJfnFlp/Y/MQfZS70w7y23okQTd/tFJRT3dWk+r+5T0dVFAcXwUAAAAAAAAAAAAAQAWQmW2Vq7MhwzAkSaZp2q4vSM3MlpebS5HjpGdZ9N+/9urjxfsKrP/o3pa6sXn1fEdZSdIHd9+gZ2Zstt3PeaaLGoX4FTv3LYcTdNdnqzVjaAfdUKuSJOnV37frf6sP2p4ZFuR91QVYElOztPHQOW04eE7/16+hrfz9BXv0waK9kqS4SYPKa3qo4AjlAAAAAAAAAAAAAABwDbJaTb2/cI/a162s+75YKyl/yGb/6WRV9nHXW3N3KTE1Sx/e01KbDyfotsmrJEnOTob2vTmw0GecOp+uL1cc0JRl+yVJQT7ueueO5jp0NlWv/rEjX/ut4/pqQ9w5Na/pr8o+7qV6n1NJ6bJapWB/j1L1u1Smaaru2Nm6EJf45tG2alUnQD9HH9a4WTG2dtvG9ZWvh2uZzAlXF0I5AAAAAAAAAAAAAABc4zKzrbKapjxcnUvU/o5PV2l93DlJUvOa/vpjZOd8bbItVtXLPXLrUhQVZjmVlK635uxWZGiAhrSqIXcXZ4WOjpIkbXipd6kDPX+XbbEq21r01yPm2HkN/HB5icaLrBOgrx9pQzgHdkoaynEqi8kAAAAAAAAAAAAAAADHc3NxKnEgR5J+GtZRI3qES5K2HknUg1+t04XNPFIzs5VlserwuTS7Pm1DA/ONs/T57lr/Ym/d1652vrrXcnebybJY9fvmo8rMtspiNfXXrpOK2npcv2w8ojEzt6nhS3NtgRxJWrjzpO36101H9Nmyfbrxv8sVOjpK0XHxxb7bgpiTqvfiHEW8nDPuytgzenr6JoWOjtL2o4m2djPWH5KkAucuSTOGtrddRx88p2bj5ispPavY5wN/x045AAAAAAAAAAAAAABcZ7YcTtDgj1fa7n8b0Um35N4HeLnqXGqW/q9vA43sWV+StP1ootbHxat/02CF+HvajfXQV+u0dM9pVfJyVULq5YVXWtSqpJcGNdIdn662K28XFqgfnuxQYJ+k9CylZVrU9s1FxY4//59d9e3qg/p2zUHFTRqk9CyLjiema31cvFIystW7UTXVCvTS23N36ZMl+2z9OtStrG8fa6vtx86raXU/uTizB8r1jOOrAAAAAAAAAAAAAABAoT5duk+T5uwqtH7PhAFycyld+CRq63GN+H5jkW0CvFz107COmjRnl/7Zp75MU7rxvyuKHfu3EZ10Q61Ktvuk9CxtPZKo+75YaysL9HZTcka2MrOtRY5Vt4q3/nque7HPtFpN1R07W5LUJjRA6+POKdjPQ2vG9iqy37GENFWv5FlkG1y9COUAAAAAAAAAAAAAAIAibTp0Trd+ssp2HxbkrQNnUvRghzoaP7jpJY15JjlD244m6mRius6lZumtuTnBn3ZhgerdqJoe6xwmJyfDrs+KvWd0/5drCxpOPwxtr7s+WyNJ2v5aP206dE4PfLmuwLZrxvRSsL+HrFZTc3ecUJ/G1XQ+LUvjZsVozf6zOp2UIUkyDOnAxEElep8LOwHlNe6mxopPydTSPafVuk6gnuxWV9X8PCRJy/ac1oNfrVOviKp6tm8DVfPzUEpGtqr6emjyklj9EH1YPw/rqFqBXkrOyJbVNOXn4VqiuaBiIJQDAAAAAAAAAAAAAACKZJqmGr8yT2lZFs0a2VnNavo7/BkZ2RZtOZyoNqEBMgyj0Haxp5IV6O2m1MxsTV93SP/oWV8WqykvN2eFjZld7HM61wvSt4+1LfIZ3689pLG/btPYgREa2jW8xO/Q7/1l2n0ySTUDPHXkXFqBbZ7r00D/6FVf78zbpY8X7yuwzQUtavqrmp+H5sectJXtntBf7i7Otvu1+88q2N9DdSp7l3ieKBuEcgAAAAAAAAAAAAAAwDXhjy3H9PT0TXZlT/esp0YhfjpyLk1PdK1b4rGSM7Ll4+5S6jmcS8mUn6erBn24XLtOJBXYpl5VH9UN8rYL25TUkJY19OaQZtp1IknDp23Q8cR01Qr01LLnexQZNELZI5QDAAAAAAAAAAAAAACuGRfyDUcT0lTJy+2SgjWOEvHyHKVnWbXl1b7ydXfR4I9XatvRRFt9twZVNHpAhMKr+CjLYtXw7zaqqq+7Jg1ppr2nkjXgg+WSco7BalGrkt0RYnkNaVlD7911Q5m8E0qOUA4AAAAAAAAAAAAAAMAVcDwxTQfOpKhjeJCknMDQ7G0nNOL7jZKkD+6+QYNvqFFo/xnrDmnqqjj9+lQnebo5Kyk9S83Gzbdrc1OL6nr1psYK8nG/ci+CS0IoBwAAAAAAAAAAAAAAoAyZpqn9Z1IUXsWn1H03H05QQmqmOtULkquz0xWYHRylpKGc8tvLCQAAAAAAAAAAAAAA4BpiGMYlBXIk6YZalRw8G5Q3olUAAAAAAAAAAAAAAACAgxHKAQAAAAAAAAAAAAAAAByMUA4AAAAAAAAAAAAAAADgYIRyAAAAAAAAAAAAAAAAAAcjlAMAAAAAAAAAAAAAAAA4GKEcAAAAAAAAAAAAAAAAwMEI5QAAAAAAAAAAAAAAAAAORigHAAAAAAAAAAAAAAAAcDBCOQAAAAAAAAAAAAAAAICDEcoBAAAAAAAAAAAAAAAAHIxQDgAAAAAAAAAAAAAAAOBghHIAAAAAAAAAAAAAAAAAByOUAwAAAAAAAAAAAAAAADgYoRwAAAAAAAAAAAAAAADAwQjlAAAAAAAAAAAAAAAAAA5GKAcAAAAAAAAAAAAAAABwMEI5AAAAAAAAAAAAAAAAgIMRygEAAAAAAAAAAAAAAAAcjFAOAAAAAAAAAAAAAAAA4GCEcgAAAAAAAAAAAAAAAAAHI5QDAAAAAAAAAAAAAAAAOBihHAAAAAAAAAAAAAAAAMDBCOUAAAAAAAAAAAAAAAAADkYoBwAAAAAAAAAAAAAAAHAwQjkAAAAAAAAAAAAAAACAgxHKAQAAAAAAAAAAAAAAAByMUA4AAAAAAAAAAAAAAADgYIRyAAAAAAAAAAAAAAAAAAcjlAMAAAAAAAAAAAAAAAA4GKEcAAAAAAAAAAAAAAAAwMEI5QAAAAAAAAAAAAAAAAAORigHAAAAAAAAAAAAAAAAcDBCOQAAAAAAAAAAAAAAAICDEcoBAAAAAAAAAAAAAAAAHIxQDgAAAAAAAAAAAAAAAOBghHIAAAAAAAAAAAAAAAAAByOUAwAAAAAAAAAAAAAAADgYoRwAAAAAAAAAAAAAAADAwQjlAAAAAAAAAAAAAAAAAA5GKAcAAAAAAAAAAAAAAABwMEI5AAAAAAAAAAAAAAAAgIMRygEAAAAAAAAAAAAAAAAcjFAOAAAAAAAAAAAAAAAA4GCEcgAAAAAAAAAAAAAAAAAHI5QDAAAAAAAAAAAAAAAAOBihHAAAAAAAAAAAAAAAAMDBCOUAAAAAAAAAAAAAAAAADkYoBwAAAAAAAAAAAAAAAHAwQjkAAAAAAAAAAAAAAACAgxHKAQAAAAAAAAAAAAAAAByMUA4AAAAAAAAAAAAAAADgYIRyAAAAAAAAAAAAAAAAAAcjlAMAAAAAAAAAAAAAAAA4GKEcAAAAAAAAAAAAAAAAwMEI5QAAAAAAAAAAAAAAAAAORigHAAAAAAAAAAAAAAAAcDBCOQAAAAAAAAAAAAAAAICDEcoBAAAAAAAAAAAAAAAAHIxQDgAAAAAAAAAAAAAAAOBghHIAAAAAAAAAAAAAAAAAByOUAwAAAAAAAAAAAAAAADgYoRwAAAAAAAAAAAAAAADAwQjlAAAAAAAAAAAAAAAAAA5GKAcAAAAAAAAAAAAAAABwMEI5AAAAAAAAAAAAAAAAgIMRygEAAAAAAAAAAAAAAAAcjFAOAAAAAAAAAAAAAAAA4GCEcgAAAAAAAAAAAAAAAAAHI5QDAAAAAAAAAAAAAAAAOBihHAAAAAAAAAAAAAAAAMDBCOUAAAAAAAAAAAAAAAAADkYoBwAAAAAAAAAAAAAAAHAwwzTN8p6DjWEYpyUdLO95wE6QpDPlPQkA1xXWHQBljXUHQFlj3QFQ1lh3AJQl1hwAZY11B0BZY92BJNUxTbNKcY0qVCgHFY9hGNGmaUaW9zwAXD9YdwCUNdYdAGWNdQdAWWPdAVCWWHMAlDXWHQBljXUHpcHxVQAAAAAAAAAAAAAAAICDEcoBAAAAAAAAAAAAAAAAHIxQDorzWXlPAMB1h3UHQFlj3QFQ1lh3AJQ11h0AZYk1B0BZY90BUNZYd1Bihmma5T0HAAAAAAAAAAAAAAAA4JrCTjkAAAAAAAAAAAAAAACAgxHKQaEMw+hvGMZuwzBiDcMYXd7zAXB1MgyjlmEYiw3D2GkYxg7DMJ7JLR9nGMZRwzA25/4ZmKfPmNy1Z7dhGP3ylLMuASgRwzDiDMPYlru+ROeWBRqGscAwjL25/w3ILTcMw/gwd23ZahhGqzzjPJTbfq9hGA+V1/sAqNgMw2iY5zPNZsMwzhuGMYrPOwAcyTCMrwzDOGUYxvY8ZQ77fGMYRuvcz0+xuX2Nsn1DABVNIevOO4Zh7MpdW341DKNSbnmoYRhpeT73fJqnT4HrS2FrGIDrVyHrjsO+rzIMI8wwjLW5684PhmG4ld3bAahoCllzfsiz3sQZhrE5t5zPOrhkhHJQIMMwnCV9LGmApMaS7jEMo3H5zgrAVSpb0nOmaTaS1F7SiDzryfumad6Q+2e2JOXW3S2piaT+kj4xDMOZdQnAJeiRu75E5t6PlrTINM36khbl3ks560r93D9DJU2Wcr5pkvSqpHaS2kp6lW+cABTENM3dFz7TSGotKVXSr7nVfN4B4ChTlbNm5OXIzzeTc9te6Pf3ZwG4/kxV/rVggaSmpmk2l7RH0pg8dfvyfO4Zlqe8sPWlsDUMwPVrqgr+DOKo76veyh2rvqRzkh67om8DoKKbqr+tOaZp3pXn33h+kTQzTzWfdXBJCOWgMG0lxZqmud80zUxJMyQNLuc5AbgKmaZ53DTNjbnXSZJ2SqpRRJfBkmaYpplhmuYBSbHKWZNYlwBcrsGS/pd7/T9Jt+Qp/8bMsUZSJcMwQiT1k7TANM140zTPKecfn/nhFIDi9FLOP9IcLKINn3cAlJppmsskxf+t2CGfb3Lr/EzTXG2apinpmzxjAbhOFbTumKY53zTN7NzbNZJqFjVGMetLYWsYgOtUIZ93ClOq76tyd67oKenn3P6sO8B1rqg1J3fNuFPS9KLG4LMOSoJQDgpTQ//f3r3GylWVcRh//rZY7vdLCCIgFIkBLIiAIqTUWgUJco0lRKqQCEhNkAQRRFBELVbRRPzENRioQsKlKggEEBLSQrm03CR4CdEKKRaQIkWFw+uHWa3TcuacFkaOhz6/pNl73ll7z9pzkjdrT9+9Fvyl6/VChv5PdEkaVpLtgT2Ae1toepvu+LKupzN75R/zkqTVUcCtSR5I8sUW26qqnoFOwSCwZYubdyT101RW/MHG8Y6k/6V+jW+2afsrxyVpKMcDN3e93iHJQ0nuSrJ/iw2VX3rlMElaWT/uqzYD/t5VWOh4R9JQ9gcWVdXvu2KOdfSmWJSjXgZbN7ze9l5IesdIsj6dqf5OraoldKbz2xGYADwD/HBZ00EOryHikjSY/apqTzpTFZ+S5IAh2pp3JPVFkncDhwLXtpDjHUkjZXXzjPlH0mpJ8nU6S5Zf1ULPAO+tqj2A04Crk2yI+UXSW9ev+yrzkaTVcQwrPnTlWEdvmkU56mUhsG3X6/cAT49QXySNcknWolOQc1VVXQdQVYuqaqCqXgcupjOtKPTOP+YlSausqp5u22eB6+nkmEVtOtFl04o+25qbdyT1y0HAg1W1CBzvSHpb9Gt8s5AVl6Ax/0jqKck04BDg2LZMA235mOfa/gPAH4GdGTq/9MphkrRcH++rFtNZ0nPsSnFJWkHLE0cAv1gWc6yjt8KiHPUyDxifZIf2tOdUYPYI90nSKNTW3bwU+F1VXdgV37qr2eHAo21/NjA1ybgkOwDjgfswL0laRUnWS7LBsn1gCp0cMxuY1ppNA25s+7OB49KxL/Bim070FmBKkk3a1MhTWkySelnhKSrHO5LeBn0Z37T3Xkqyb7uHO67rXJK0XJJPAWcAh1bV0q74FknGtP330Rnf/GmY/NIrh0nScv26r2pFhHcCR7XjzTuSepkMPFFVy5elcqyjt2Ls8E20Jqqq15JMp/NjzRjgsqp6bIS7JWl02g/4HPBIkvktdhZwTJIJdKbxewo4EaCqHktyDfA4nWmQT6mqAQDzkqRVtBVwfeceiLHA1VX1myTzgGuSnAD8GTi6tb8JOBj4A7AU+AJAVT2f5Nt0fswBOK+qnn/7LkPSaJJkXeATtDFN833HO5L6JcksYCKweZKFwLnADPo3vjkZuAJYB7i5/ZO0BuuRd84ExgG3tXuuuVV1EnAAcF6S14AB4KRVyC+9cpikNVSPvDOxj/dVZwA/T3I+8BCdh0klraEGyzlVdSmdYr5ZKzV3rKM3LW12SUmSJEmSJEmSJEmSJEl94vJVkiRJkiRJkiRJkiRJUp9ZlCNJkiRJkiRJkiRJkiT1mUU5kiRJkiRJkiRJkiRJUp9ZlCNJkiRJkiRJkiRJkiT1mUU5kiRJkiRJkiRJkiRJUp9ZlCNJkiRJkjSMJANJ5idZkOTBJB8dpv3GSb60Cuf9bZK9+tfT0S/JFUmOGul+SJIkSZIkvVUW5UiSJEmSJA3vlaqaUFUfBM4EvjdM+42BYYtyRkqSsSPdB0mSJEmSpHc6i3IkSZIkSZJWz4bACwBJ1k9ye5s955Ekn2ltZgA7ttl1Zra2X21tFiSZ0XW+o5Pcl+TJJPu3tmOSzEwyL8nDSU5s8a2T3N3O++iy9t2SPJXkgnbO+5Ls1OJXJLkwyZ3ABUk2TXJDO//cJLt3XdPlra8PJzmyxackmdOu9dok67f4jCSPt7Y/aLGjW/8WJLl7mGtKkovaOX4NbNnPP5YkSZIkSdJI8akoSZIkSZKk4a2TZD6wNrA1MKnF/wkcXlVLkmwOzE0yG/gasGtVTQBIchBwGLBPVS1NsmnXucdW1d5JDgbOBSYDJwAvVtWHk4wD7klyK3AEcEtVfSfJGGDdHv1d0s55HPBj4JAW3xmYXFUDSX4CPFRVhyWZBFwJTAC+0T57t9b3Tdq1nd2OfTnJGcBpSS4CDgd2qapKsnH7nHOAWhQUHwAAAydJREFUT1bVX7tiva5pD+D9wG7AVsDjwGWr9FeRJEmSJEn6P2ZRjiRJkiRJ0vBe6Sqw+QhwZZJdgQDfTXIA8DqwDZ3CkpVNBi6vqqUAVfV813vXte0DwPZtfwqwe5Kj2uuNgPHAPOCyJGsBN1TV/B79ndW1/VFX/NqqGmj7HwOObP25I8lmSTZqfZ267ICqeiHJIcAH6BTSALwbmAMsoVOYdEmb5eZX7bB7gCuSXNN1fb2u6QBgVuvX00nu6HFNkiRJkiRJo4pFOZIkSZIkSauhqua0mWO2AA5u2w9V1atJnqIzm87KAlSPU/6rbQf47281Ab5cVbe84USdAqBPAz9LMrOqrhysmz32X16pT4MdN1hfA9xWVccM0p+9gY/TKeSZDkyqqpOS7NP6OT/JhF7X1GYI6vXdSJIkSZIkjVrvGukOSJIkSZIkjSZJdgHGAM/Rme3l2VaQcyCwXWv2ErBB12G3AscnWbedo3v5qsHcApzcZsQhyc5J1kuyXfu8i4FLgT17HP/Zru2cHm3uBo5t558ILK6qJa2v07uudxNgLrBfkp1abN3Wp/WBjarqJuBUOstfkWTHqrq3qs4BFgPb9rqm1o+pScYk2Ro4cJjvRpIkSZIkaVRwphxJkiRJkqThrZNk2VJRAaZV1UCSq4BfJrkfmA88AVBVzyW5J8mjwM1VdXqbLeb+JP8GbgLOGuLzLqGzlNWD6awX9TfgMGAicHqSV4F/AMf1OH5cknvpPJD1htltmm8Clyd5GFgKTGvx84Gftr4PAN+qquuSfB6YlWRca3c2neKjG5Os3b6Xr7T3ZiYZ32K3AwuAh3tc0/XAJOAR4EngriG+F0mSJEmSpFEjVc4OLEmSJEmS9E7RltDaq6oWj3RfJEmSJEmS1mQuXyVJkiRJkiRJkiRJkiT1mTPlSJIkSZIkSZIkSZIkSX3mTDmSJEmSJEmSJEmSJElSn1mUI0mSJEmSJEmSJEmSJPWZRTmSJEmSJEmSJEmSJElSn1mUI0mSJEmSJEmSJEmSJPWZRTmSJEmSJEmSJEmSJElSn1mUI0mSJEmSJEmSJEmSJPXZfwANC9WcFlzZVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2880x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "l.save(f\"speedup_{optimizer}_batch_norm_{batch_norm}_{loss_func}_nlayers_{len(layers_sizes)}_log_{log}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'done'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"done\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_df = get_results_df(val_dl, l.model)\n",
    "train_df = get_results_df(train_dl, l.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = val_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>185651.000000</td>\n",
       "      <td>185651.000000</td>\n",
       "      <td>185651.000000</td>\n",
       "      <td>185651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.094639</td>\n",
       "      <td>1.184810</td>\n",
       "      <td>0.498368</td>\n",
       "      <td>160.315704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.212444</td>\n",
       "      <td>1.613917</td>\n",
       "      <td>0.622952</td>\n",
       "      <td>341.550537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.244995</td>\n",
       "      <td>0.008491</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.001805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.337003</td>\n",
       "      <td>0.211613</td>\n",
       "      <td>0.154713</td>\n",
       "      <td>22.529292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.613558</td>\n",
       "      <td>0.565550</td>\n",
       "      <td>0.292336</td>\n",
       "      <td>48.972534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.312282</td>\n",
       "      <td>1.394052</td>\n",
       "      <td>0.606106</td>\n",
       "      <td>127.872387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.475633</td>\n",
       "      <td>16.089287</td>\n",
       "      <td>14.893134</td>\n",
       "      <td>6498.653320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          prediction         target       abs_diff            APE\n",
       "count  185651.000000  185651.000000  185651.000000  185651.000000\n",
       "mean        1.094639       1.184810       0.498368     160.315704\n",
       "std         1.212444       1.613917       0.622952     341.550537\n",
       "min         0.244995       0.008491       0.000017       0.001805\n",
       "25%         0.337003       0.211613       0.154713      22.529292\n",
       "50%         0.613558       0.565550       0.292336      48.972534\n",
       "75%         1.312282       1.394052       0.606106     127.872387\n",
       "max         7.475633      16.089287      14.893134    6498.653320"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>185651.000000</td>\n",
       "      <td>185651.000000</td>\n",
       "      <td>185651.000000</td>\n",
       "      <td>185651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.094639</td>\n",
       "      <td>1.184810</td>\n",
       "      <td>0.498368</td>\n",
       "      <td>160.315704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.212444</td>\n",
       "      <td>1.613917</td>\n",
       "      <td>0.622952</td>\n",
       "      <td>341.550537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.244995</td>\n",
       "      <td>0.008491</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.001805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.337003</td>\n",
       "      <td>0.211613</td>\n",
       "      <td>0.154713</td>\n",
       "      <td>22.529292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.613558</td>\n",
       "      <td>0.565550</td>\n",
       "      <td>0.292336</td>\n",
       "      <td>48.972534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.312282</td>\n",
       "      <td>1.394052</td>\n",
       "      <td>0.606106</td>\n",
       "      <td>127.872387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.475633</td>\n",
       "      <td>16.089287</td>\n",
       "      <td>14.893134</td>\n",
       "      <td>6498.653320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          prediction         target       abs_diff            APE\n",
       "count  185651.000000  185651.000000  185651.000000  185651.000000\n",
       "mean        1.094639       1.184810       0.498368     160.315704\n",
       "std         1.212444       1.613917       0.622952     341.550537\n",
       "min         0.244995       0.008491       0.000017       0.001805\n",
       "25%         0.337003       0.211613       0.154713      22.529292\n",
       "50%         0.613558       0.565550       0.292336      48.972534\n",
       "75%         1.312282       1.394052       0.606106     127.872387\n",
       "max         7.475633      16.089287      14.893134    6498.653320"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5272.000000</td>\n",
       "      <td>5272.000000</td>\n",
       "      <td>5272.000000</td>\n",
       "      <td>5272.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.139454</td>\n",
       "      <td>5.371452</td>\n",
       "      <td>0.573196</td>\n",
       "      <td>24.085110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.129367</td>\n",
       "      <td>2.290008</td>\n",
       "      <td>0.491706</td>\n",
       "      <td>81.973442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.541461</td>\n",
       "      <td>0.058317</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.001177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.618636</td>\n",
       "      <td>3.869889</td>\n",
       "      <td>0.205381</td>\n",
       "      <td>4.140809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.469416</td>\n",
       "      <td>5.639095</td>\n",
       "      <td>0.451528</td>\n",
       "      <td>8.874032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.063024</td>\n",
       "      <td>6.547940</td>\n",
       "      <td>0.809658</td>\n",
       "      <td>15.718910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.828941</td>\n",
       "      <td>13.560771</td>\n",
       "      <td>3.484350</td>\n",
       "      <td>828.611633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prediction       target     abs_diff          APE\n",
       "count  5272.000000  5272.000000  5272.000000  5272.000000\n",
       "mean      5.139454     5.371452     0.573196    24.085110\n",
       "std       2.129367     2.290008     0.491706    81.973442\n",
       "min       0.541461     0.058317     0.000058     0.001177\n",
       "25%       4.618636     3.869889     0.205381     4.140809\n",
       "50%       5.469416     5.639095     0.451528     8.874032\n",
       "75%       6.063024     6.547940     0.809658    15.718910\n",
       "max      10.828941    13.560771     3.484350   828.611633"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange == 0) & (df.tile == 0) & (df.unroll == 1)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df[(df.interchange == 0) & (df.tile == 0) & (df.unroll == 1)]\n",
    "#joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df2 = df\n",
    "joint_plot(df2, f\"Validation dataset, {loss_func} loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ = df.sort_values(by=[\"target\"])\n",
    "\n",
    "df_['x'] = range(len(df_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8f85b77be0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot('x', 'prediction', 'bo', data=df_)\n",
    "plt.plot('x', 'target','ro', data=df_)\n",
    "\n",
    "plt.xlabel('scheduled program')\n",
    "plt.ylabel('speedup')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8f85b5f5c0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot('x', 'abs_diff', 'g-', data=df_)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
